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Relationships Between Learners’ Personality Traits and Transactional Distance

within an e-Learning Environment

Submitted by

Burton Alexander Casteel, III

A Dissertation Presented in Partial Fulfillment

of the Requirements for the Degree

Doctorate of Philosophy

Grand Canyon University

Phoenix, Arizona

August 26, 2016

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GRAND CANYON UNIVERSITY

Relationships Between Learners’ Personality Traits and Transactional Distance

within an e-Learning Environment

Burton Alexander Casteel, III

Approved

August 15, 2016

DISSERTATION COMMITTEE:

Audrey Rabas, Ph.D., Dissertation Chair

Julie Nelson, Ph.D., Committee Member

Nathan Griffith, Ph.D., Committee Member

ACCEPTED AND SIGNED:

________________________________________ Michael R. Berger, Ed.D. Dean, College of Doctoral Studies

_________________________________________ Date

GRAND CANYON UNIVERSITY

Relationships Between Learners’ Personality Traits and Transactional Distance

within an e-Learning Environment

I verify that my dissertation represents original research, is not falsified or plagiarized,

and that I have accurately reported, cited, and referenced all sources within this

manuscript in strict compliance with APA and Grand Canyon University (GCU)

guidelines. I also verify my dissertation complies with the approval(s) granted for this

research investigation by GCU Institutional Review Board (IRB).

_____________________________________________ ______August 4, 2016_____ Burton A. Casteel, III Date

Abstract

The relationship between personality traits and learner outcomes has been demonstrated

within a variety of environments. However, the extent of the relationship between Five-

Factor Model personality traits and transactional distance had not previously been

examined within the asynchronous video e-learning environment. It was not known if

personality traits were predictive of transactional distance in this environment. This

question was addressed through a quantitative correlational study conducted online using

an interactive course. Participants (N= 98) were recruited online from across the U.S.

All participants completed the Big Five Inventory, three modules of a video-based

communications course, and the Structure Component Evaluation Tool (SCET), a

measure of transactional distance (TD) in which high scores indicate more desirable or

small transactional distance. Pearson correlation analysis was conducted between each

personality trait and SCET values to measure the relationship. It was found that

Openness (r = .25, N = 98, p = .02) and Extroversion (r = .28, N = 98, p = .005) exhibited

significant positive correlations with SCET scores; therefore, as the strength of these

personality traits increased, the transactional distance decreased. Regression analysis

demonstrated that personality traits were predictive of TD (F(5, 92) = 3.99, p = .003, R2 =

.18, Adjusted R2 = .13) and that Extroversion (R2 = .08, p = .005) and Openness (R2 =

.062, p = .01) independently explained 14.2% of transactional distance variance. Based

upon the findings, instructional developers should consider the role of personality traits

during the creation of video-based instructional material.

Keywords: Five-Factor Model of personality traits, Transactional Distance

Theory, video, e-learning, Big Five Inventory, Structure Component Evaluation Tool

Dedication

To my wife and best friend, Jenny. Thank you for your love, patience, and

encouragement. And for playing Mario Kart with me.

vii

Acknowledgments

Over the course of my doctoral journey, I have received support and

encouragement from a great number of individuals. I am grateful for my dissertation

committee, Dr. Audrey Rabas, Dr. Julie Nelson, and Dr. Nathan Griffith, for their

tremendous guidance, encouragement, and accountability throughout my research and

writing. Your counsel throughout the study process exemplified the spirit of the learning

journey, and I am a better scholar for it. Thank you to Dr. Andree Robinson-Neal and

Dr. George Bradley for your thorough reviews of my writing. Thank you to my fellow

doctoral students for your support, feedback, and friendship. I am also thankful for my

good friend, Dr. Kurt Peters, who helped me greatly by authoring some of the code

within my study instrument. I am thankful for my TNS friends, Kelly, Scott, Tim, and

Kyle, who challenged me to squeeze tighter and aim. Last, but certainly not least, I

would like to thank my family, especially my wife, Jenny Casteel, and daughters Katie,

Megan, and Sydney Casteel, for your love, encouragement, and the late night cookies.

viii

Table of Contents

List of Tables ..................................................................................................................... xii

List of Figures ................................................................................................................... xiii

Chapter 1: Introduction to the Study ................................................................................... 1

Introduction ................................................................................................................... 1

Background of the Study ............................................................................................... 6

Problem Statement ....................................................................................................... 10

Purpose of the Study .................................................................................................... 13

Research Questions and Hypotheses ........................................................................... 16

Advancing Scientific Knowledge ................................................................................ 19

Significance of the Study ............................................................................................. 23

Rationale for Methodology .......................................................................................... 25

Nature of the Research Design for the Study .............................................................. 28

Definition of Terms ..................................................................................................... 34

Assumptions, Limitations, Delimitations .................................................................... 38

Summary and Organization of the Remainder of the Study ........................................ 40

Chapter 2: Literature Review ............................................................................................ 44

Introduction to the Chapter and Background to the Problem ...................................... 44

Theoretical Foundations and Conceptual Framework ................................................. 48

Review of the Literature .............................................................................................. 56

Characteristics of learning .................................................................................. 58

Learning environments ...................................................................................... 64

Psychological constructs in the e-learning environment .................................... 78

Personality and learning ..................................................................................... 81

Methodology ....................................................................................................... 91

Instrumentation. .................................................................................................. 99

Summary .................................................................................................................... 105

Chapter 3: Methodology .................................................................................................. 110

Introduction ............................................................................................................... 110

Statement of the Problem .......................................................................................... 111

Research Questions and Hypotheses ......................................................................... 111

Research Methodology .............................................................................................. 114

Research Design ........................................................................................................ 119

Population and Sample Selection .............................................................................. 123

Instrumentation .......................................................................................................... 126

Validity ...................................................................................................................... 128

Reliability .................................................................................................................. 129

Data Collection and Management ............................................................................. 130

Data Analysis Procedures .......................................................................................... 135

Ethical Considerations ............................................................................................... 140

Limitations and Delimitations ................................................................................... 141

Summary .................................................................................................................... 142

Chapter 4: Data Analysis and Results ............................................................................. 147

Introduction ............................................................................................................... 147

Descriptive Data ........................................................................................................ 148

Tests of linearity and normality ........................................................................ 155

Test of homoscedasticity .................................................................................. 156

Data Analysis Procedures .......................................................................................... 156

Research Question 1 and hypotheses ................................................................ 162

Research Question 2 and hypotheses ................................................................ 162

Additional analyses ........................................................................................... 163

Results ....................................................................................................................... 163

Research Question 1 and hypotheses ................................................................ 164

Research Question 2 and hypotheses ................................................................ 165

Additional findings in Chapters 4 and 5 ........................................................... 168

Summary .................................................................................................................... 171

Chapter 5: Summary, Conclusions, and Recommendations ........................................... 173

Introduction ............................................................................................................... 173

Summary of the Study ............................................................................................... 175

Summary of Findings and Conclusion ...................................................................... 178

Research Question 1 and hypotheses ................................................................ 178

Research Question 2 and hypotheses ................................................................ 181

Additional findings in Chapters 4 and 5 ........................................................... 184

Implications ............................................................................................................... 186

Theoretical implications ................................................................................... 186

Practical implications ........................................................................................ 188

Future implications ........................................................................................... 191

Strengths and weaknesses ................................................................................. 192

Recommendations ..................................................................................................... 196

Recommendations for future research .............................................................. 196

Recommendations for future practice ............................................................... 199

References ....................................................................................................................... 202

Appendix A. IRB Approval Letter .................................................................................. 228

Appendix B. Informed Consent ....................................................................................... 229

Appendix C. Copy of Instruments and Permissions Letters to Use the Instruments ...... 232

Appendix D. Recruitment Script ..................................................................................... 241

Appendix E. Recruitment Materials ................................................................................ 243

Appendix F. Tables and Charts for Statistical Analyses ................................................. 246

Appendix G. Statistical Analyses .................................................................................... 259

xii

List of Tables

Table 1. Online Course and Survey Continuation and Completion Data ........................ 151

Table 2. Participant Demographics (N = 98) ................................................................... 154

Table 3. Descriptive Statistics of Participant Personality Traits and TD Measures ........ 155

Table 4. Reliability of Big Five Inventory Scale ............................................................. 161

Table 5. Comparison of Personality Traits for Sample and General Populations ........... 161

Table 6. Pearson Correlations between FFM Personality Traits and SCET Values ....... 165

Table 7. Multiple Regression Analysis of SCET Values by FFM Personality Traits ..... 167

Table 8. Hierarchical Regression Analysis for FFM Personality Traits and SCET Values ............................................................................................................................ 167

Table 9. Independent Samples t-Test of Internet Experience with SCET Values ........... 169

Table 10. Independent Samples Test of Gender with SCET Values ............................... 169

Table 11. Analysis of Variation between Device Type and SCET Values ..................... 170

Table F1. Tests of Normality for Participant Personality Traits and TD Measures ........ 257

Table F2. Test of Homogeneity of Variances ................................................................. 258

Table F3. Personality Trait Collinearity Statistics .......................................................... 259

Table G1. Group Statistics of Internet Experience with SCET Values ........................... 259

Table G2. Group Statistics for Gender with SCET Values ............................................. 259

Table G3. Descriptive Statistics for Device Type with SCET Values ............................ 259

xiii

List of Figures

Figure 1. Workflow describing learner path and data collection .................................... 133

Figure C1. SCET permission letter. ............................................................................... 239

Figure E1. Ad #1 of Google AdWords campaign ........................................................... 243

Figure E2. Ad #2 of Google AdWords campaign ........................................................... 243

Figure E3. Ad #3 of Google AdWords campaign ........................................................... 244

Figure E4. Ad for paid Facebook social media recruitment campaign ........................... 244

Figure E5. Front side of recruitment postcard ................................................................. 245

Figure E6. Back side of recruitment postcard ................................................................. 245

Figure F1. Histogram of trait Openness within sample population ................................. 246

Figure F2. Box chart for trait Openness from sample population ................................... 247

Figure F3. Normal Q-Q plot of trait Openness from sample population ........................ 247

Figure F4. Histogram of trait Conscientiousness within sample population ................... 248

Figure F5. Box plot of trait Conscientiousness from sample population ........................ 249

Figure F6. Normal Q-Q plot of trait Conscientiousness from sample population .......... 249

Figure F7. Histogram of trait Extroversion within sample population. .......................... 250

Figure F8. Box plot of trait Extroversion from sample population ................................. 251

Figure F9. Normal Q-Q plot of trait Extroversion from sample population ................... 251

Figure F10. Histogram of trait Agreeableness within sample population ....................... 252

Figure F11. Box plot of trait Agreeableness from sample population ............................ 252

Figure F12. Normal Q-Q plot for trait Agreeableness from sample population ............. 253

Figure F13. Histogram for trait Neuroticism within sample population ......................... 253

Figure F14. Box plot for trait Neuroticism from sample population. ............................. 254

Figure F15. Normal Q-Q plot for trait Neuroticism from sample population ................. 254

xiv

Figure F16. Histogram for SCET values within sample population with observed right skewness (1.02) .................................................................................................. 255

Figure F17. Box plot for SCET values from sample population. .................................... 256

Figure F18. Normal Q-Q plot for SCET values from sample population with nonparametric values. ......................................................................................... 256

Figure F19. Linearity test using scatterplot for personality traits and SCET values.. ..... 257

Chapter 1: Introduction to the Study

Introduction

The extent of the fit between the learner and learning environment factors (Wu &

Hwang, 2010), such as a video instructor (Kim & Thayne, 2015), on-screen, multimedia

content (Calli, Balcikanli, Calli, Cebeci, & Seymen, 2013), or peer interaction (Wang &

Morgan, 2008), within each learning environment is a critical determinant in student

learning outcomes. The more satisfying, attractive, and useful the learning factors are to

the learner, the more likely the student is to interact with the learning environment, and

ask questions, clarify information, and remain open to new information, and,

subsequently, to perform well (Hauser, Paul, & Bradley, 2012; Wang, Chen, &

Anderson, 2014). Moore’s (1993) Transactional Distance Theory introduced three types

of learner interactions that occur within the distance-learning environment, which are

between learner and instructor, between learners, and between the learner and the

content. Chen (2001) identified the interaction between the learner and the technological

interface as a fourth interaction type. The intensity and quality of the learner’s

interaction experience with the learning environment is measured as transactional

distance (TD), which is the learner’s perceived psychological and communication

distance between the learner and the learning environment (Ustati & Hassan, 2013).

Environments in which the learner perceives easier communication and more comfortable

interactions are characterized by small TD, while environments in which the learner finds

it difficult to ask question or obtain the desired information are marked by large TD

(Moore, 1993). The desired relationship between the learner and the learning

environment is to have as small a TD as possible, a relationship that facilitates the

greatest opportunity for a learner to explore and clarify information (Benson &

Samarawickrema, 2009). Transactional distance is influenced by three design factors: the

structure of the environment, the amount and frequency of purposeful and valuable

communication between the learner and learning environment, and the learner’s

autonomy within the environment (Chen, 2001; Park, 2011).

Self-regulatory processes—those psychological characteristics that govern an

individual’s behavior—are also responsible in part for a learner’s interaction experience

and the resulting transactional distance (Moore, 1993). Psychological constructs that

influence self-regulation include personality traits (Legault & Inzlicht, 2013), self-

esteem, self-efficacy, motivation, and attitudes (Fishman, 2014). Individual learner self-

regulatory processes, including self-efficacy (Hauser et al., 2012), attitudes (Wu &

Hwang, 2010), and motivation (Byun, 2014), were correlated to the learner’s personality

traits (Tabak & Nguyen, 2013) and were shown to influence the learner’s propensity to

engage in dialogue and exhibit autonomy within the distance learning environment.

Current studies assessed the relationship between learner personality traits and the

learning environment. Five-Factor Model (FFM) personality traits have been shown to

correlate with learner-learning environment interaction quality and strength in some

distance-learning environments, including two-way video distance learning (Falloon,

2011), hybrid online and in-seat classrooms (Al-Dujaily, Kim, & Ryu, 2013; Murphy &

Rodríguez-Manzanares, 2008), asynchronous computer-assisted instruction (Kickul &

Kickul, 2006), and game-based learning (Bauer, Brusso, & Orvis, 2012). Studies such as

these contributed to a holistic view of the learner-learning environment interaction within

the e-learning environment by providing a map from the most basic of human

characteristics—one’s personality—to that person’s interaction preferences within a

learning environment. Additionally, considering the fit between personality traits and

various e-learning settings extended the conclusions of Benson and Samarawickrema

(2009) for instructional designers to determine the environment most preferred by the

learner to reduce communication difficulties and meet the designer’s desired level of

learner autonomy to include learner self-regulatory processes. Because the learner’s

natural tendencies tend not to change (Mōttus, Johnson, & Deary, 2012), the learning

environment must adapt in order to maximize learning interaction and improve learner

performance. Developing a complete map of the learning topography between human

characteristics and knowledge acquisition is a grand endeavor, one that will be achieved

incrementally with each related study.

Bolliger and Erichsen (2013) investigated the relationship between Myers-Briggs

Type Indicator (MBTI) personality types and student satisfaction with learning

interactions within a broad range of technologically diverse online and blended settings.

Although the authors concluded that personality types correlated with learner satisfaction

levels within differing learning environments, Bolliger and Erichsen identified a gap in

the extant research. Specifically, the authors recommended future research exploring the

relationship between personality characteristics and learner satisfaction with learning

interactions within different settings, with different audiences, or with larger sample sizes

in order to generalize the results. A unique setting is asynchronous video e-learning,

which is an emerging method of instruction that integrates video content with embedded,

online reinforcement activities, such as quizzes, applications, and writing (Stigler, Geller,

& Givvin, 2015), providing a content-rich, entertaining, and efficient environment for

increased engagement (Ljubojevic, Vaskovic, Stankovic, & Vaskovic, 2014). The

current study sought to address the gap identified by Bolliger and Erichsen (2013) and

examined the unknown relationship between personality characteristics, using Five-

Factor Model traits, and learner interaction satisfaction as measured by transactional

distance within the previously unexplored setting of asynchronous video e-learning.

The present study examined the correlation and strength of relationships between

Five-Factor Model personality traits, which have been associated with positive

performance in video environments (Barkhi & Brozovsky, 2003; Borup, West, &

Graham, 2013; Tsan & Day, 2007), and transactional distance within the asynchronous

video e-learning environment. Using quantitative methods and a correlational research

design, the study measured the Five-Factor Model personality traits of a sample

population using the Big Five Inventory (BFI; John, 2009), and compared those trait

strengths to the participants’ transactional distance as measured by the Structure

Component Evaluation Test (SCET; Sandoe, 2005) following participant involvement in

a short series of online video course segments. Scores for each trait within the BFI were

measured along a bipolar scale with scores below the midpoint indicating an absence of

the described trait (e.g., introversion) and scores higher than the midpoint indicated a

presence of the described trait (e.g., extroversion). SCET values and transactional

distance are negatively correlated such that higher scores for SCET described a smaller

transactional distance and lower SCET values indicated a larger gap psychological and

communication gap between the learner and the learning environment. As a result, a

positive correlation between a trait and a SCET value describes a negative correlation

between the trait and transactional distance. For example, if trait Extroversion is

positively correlated with SCET values, then Extroversion is negatively correlated with

transactional distance. In this example, high Extroversion scores suggests that the learner

experienced a high-quality interaction with the learning environment and low

Extroversion scores indicate the learner experienced a larger TD with a lower-quality

interaction with the learning environment. The present research design is based upon

Kim (2013) which compared personality traits and learner academic outcomes, as well as

Kolb learning styles and learner academic outcomes, following the completion of a

communications course within a blended online and in-class environment.

The results addressed the questions of whether personality traits were correlated

with a learner’s transactional distance within the asynchronous video environment.

Understanding the learner-learning environment interaction in this environment added to

the compendium of knowledge useful for instructional designers in creating an

environment conducive to more satisfying interactions between the learner and the

knowledge source. Additionally, the results of this study extended the scholarly literature

regarding personality trait-learner interaction, particularly as it applied to distance

learning and Transactional Distance Theory, by examining the perceived sense of

improved dialogue due to personality interactions with asynchronous video, resulting in

smaller pedagogical distances.

The remainder of the first chapter is organized to provide the reader an overview

of the research. The discussion begins with a description of the study’s background, the

problem statement that emerges from the literature, the purpose of the study, and the

research questions and hypotheses. Support for the research purpose is summarized in

the sections that follow, which include how the study advances scientific knowledge and

the significance of the study. The introductory chapter continues by defining the

proposed methodology for investigating the research questions and by describing the

nature of the research design that will be employed. The chapter concludes by providing

boundaries to the study through the definition of terms and through statements of the

study’s assumptions, limitations, and delimitations.

Background of the Study

A growing body of literature described a variety of theories and approaches that

associated learner characteristics and behaviors with learning outcomes. Theories about

active learning posited that individuals who engaged in learning activities saw increased

performance (Lucas, Testman, Hoyland, Kimble, & Euler, 2013); however, not all

learners engaged equally with the activity, differences that may be explained by self-

efficacy (Hauser et al., 2012), attitudes (Wu & Hwang, 2010), and motivation (Byun,

2014), self-regulatory processes that are positively associated with personality traits

(Caprara, Vecchione, Alessandri, Gerbino, & Barbaranelli, 2011; Donche, De Maeyer,

Coertjens, Van Daal, & Petegem, 2013; Hetland, Saksvik, Albertsen, Berntsen, &

Henriksen, 2012). Attempts to correlate outcomes and learning styles, which were based

upon learner preferences for feeling, watching, thinking, and doing (Chen, Jones, &

Moreland, 2014), have also met with mixed results. Some investigations described

strong correlations between the learning style and performance in traditional classrooms

(Bhatti & Bart, 2013; Black & Kassaye, 2014; Moayyeri, 2015) and in online

environments (Hwang, Sung, Hung, & Huang, 2013; Page & Webb, 2013; Richmond &

Conrad, 2012), while others demonstrated a lack of correlation (Alghasham, 2012;

Breckler, Teoh, & Role, 2011; Hsieh, Mache, & Knudson, 2012). However, correlational

differences might be reconciled when learning style is examined as a function of

personality traits, suggesting performance within a learning environment is more closely

related to personality traits than the incumbent learning style (Giannakos,

Chorianopoulos, Ronchetti, Szegedi, & Teasley, 2014; Kim, 2013).

Moore’s (1993) Transactional Distance Theory (TDT) offers that the quality and

intensity of the interaction between the learner and the learning environment influences

performance within distance learning environments. Learners who experience higher

quality interactions as indicated by small transactional distances with the instructional

source performed better than learners that experience a wider psychological or

communication gap with the knowledge source (Hauser et al., 2012). The learner’s

interaction with the learning environment is measured as transactional distance (TD),

which is described as the perceived pedagogical, psychological, and communication

distance between the learner and the learning environment as determined by the learner’s

perceived openness of dialogue, the student’s sense of autonomy within the learning

setting, and the learner’s perception of the learning structure’s flexibility (Chen, 2001;

Moore, 1993; Park, 2011). Active learning, theories on learning style, and Transactional

Distance Theory share common themes. Each theory suggests learning interaction is

influenced by characteristics of the learner and by factors within the learning

environment. Active learning describes variables of behavioral, cognitive, and social

engagement within the learning setting (Drew & Mackie, 2011), and learning style

variables include the learner’s physiological and psychological constructs, and the

learner’s response to the learning environment (Yenice, 2012). TDT’s factors of

dialogue, learner autonomy, and learning structure are defined by the specific learning

environment, and each learner’s unique characteristics (Moore, 1993). Each of the three

theories suggests the quality and intensity of the learner-learning environment interaction

is a function of the learner’s individual characteristics and the factors present within each

unique environment (Ustati & Hassan, 2013).

Kickul and Kickul (2006) found that proactive personality traits, which are

defined by Crant, Kim, and Wang (2011) as the characteristics of one who scans for

opportunities and persists to bring about closure, influenced the quality of learning and

satisfaction within computer-assisted instruction (CAI) learning environments. Hauser,

Paul, and Bradley (2012) demonstrated that computer self-efficacy and anxiety

moderated learner performance in a hybrid online and in-seat management information

systems class. Using the MBTI personality inventory, Al-Dujaily, Kim, and Ryu (2013)

showed types Extroversion, Intuitive, and Thinking were predictors of procedural

knowledge performance, while types Intuitive and Feeling were indicative of declarative

knowledge performance within CAI learning environments. Orvis, Brusso, Wasserman,

and Fisher (2011) correlated FFM trait Extroversion and trait Openness to Experience

with learner autonomy as measured by training performance in an undergraduate

management course. In gaming-based learning environments, traits Openness to

Experience and Neuroticism interacted with task difficulty conditions to determine

performance (Bauer et al., 2012).

Both Orvis et al. (2011) and Al-Dujaily et al. (2013) recommended broadening

personality research to other e-learning environments to gain greater understanding of the

relationship between personality and interaction in online learning. Bolliger and Erichsen

(2013) correlated MBTI personality types and learner interaction within a variety of

online and blended environments, demonstrating that type Sensor was related to

satisfaction with dialogue tools and independent projects, and that type Intuitive showed

interaction preferences based upon learning environment, favoring online instruction over

blended environments. Bolliger and Erichsen identified a gap in the correlational

research between personality characteristics and learner interaction satisfaction within

emerging technologies and new learning environments, and recommended that such

research should be conducted.

The extant literature examined the relationship between personality traits and

transactional distance within a variety of environments. Although the personality

characteristic measurement scale has varied within the literature, such as Myers Briggs

types (Al-Dujaily et al., 2013; Bolliger & Erichsen, 2013) and Big Five (Orvis, Brusso,

Wasserman, & Fisher, 2011), personality traits remained a central interest of exploration

as a condition within learning research, as traits are a stable facet of human behavior

(Wortman, Lucas, & Donnellan, 2012). Research focusing on learner outcomes also

remained consistent, including study of performance (Lucas et al., 2013; Thomas &

Macias-Moriarity, 2014), attitudes (Killian & Bastas, 2015; Wu & Hwang, 2010),

satisfaction (Bolliger & Erichsen, 2013), and engagement levels (Rodríguez Montequín,

Mesa Fernández, Balsera, & García Nieto, 2013), proving learner outcomes to be an

appropriate variable for comparison. The recent research focused on analysis of learners’

interactions with the learning environment by examining the relationship between

personality traits and transactional distance within a variety of learning circumstances.

The variety of variables examined produced results such that outcomes vary from one

environment type to the next. As a result, it is imperative to examine the relationship

between personality traits and transactional distance within each environment so that a

comprehensive theory may be proposed. Thus far, the literature has examined

environments of computer-aided instruction (Kickul & Kickul, 2006), game-based

learning (Bauer et al., 2012), hybrid learning structures (Moffett & Mill, 2014; Velegol,

Zappe, & Mahoney, 2015), blended learning (Bolliger & Erichsen, 2013), face-to-face

learning (Al-Dujaily et al., 2013), and two-way video distance learning (Chen & Willits,

1998; Falloon, 2011).

One environment that was not examined for the relationship between personality

traits and TD was the asynchronous video-based e-learning, a submarket of the $23.8

billion North American e-learning industry (Docebo, 2014), and a niche in which video-

based commercial ventures are growing at a rate of 100% per year (Bersin, 2012). As an

emerging framework of e-learning, asynchronous video integrates video media with

interactive activities to engage learners as a primary form of content delivery (Stigler et

al., 2015). The current study was influenced by the direction of research identified by Al-

Dujaily et al. (2013) and Orvis et al. (2011), and the specific gap identified by Bolliger

and Erichsen (2013). Although the literature explored the relationship between

personality and learner outcomes within a variety of distant learning formats, the question

of if personality traits correlate with transactional distance within asynchronous video-

based e-learning was unknown.

Problem Statement

It was not known if and to what degree personality traits correlate with a learner’s

perceived transactional distance within an asynchronous video-based e-learning

environment. The literature demonstrated that personality traits correlated with TD

within asynchronous computer-assisted instruction environments (Kickul & Kickul,

2006), high- and low-autonomy conditions of CAI (Orvis et al., 2011), hybrid CAI and

in-seat environments (Hauser et al., 2012), and gaming-based learning environments

(Bauer et al., 2012), and MBTI personality types correlated with interaction satisfaction

in blended environments (Bolliger & Erichsen, 2013). Because individuals with differing

personality traits demonstrated preferences for diverse learning environments, and

matching learners with engaging learning environments maximized the individual’s

achievement opportunity (Kim, 2013), it is important for instructional designers to design

courses with the appropriate levels of dialogue and structure for the learners in order to

reduce transactional distance based upon learner characteristics (Benson &

Samarawickrema, 2009). This research added to the portfolio of available instructional

design tools for aligning personality traits and learning environments while addressing

the gap in the research as described by Bolliger and Erichsen (2013).

The established research examined the relationship that exists between personality

traits and learner outcomes and behaviors with a focus on the learning environment. As a

result, the variables of personality traits have remained consistent within the research, as

have the variables of learner outcomes, such as interaction (Rodríguez Montequín et al.,

2013), performance (Lucas et al., 2013; Thomas & Macias-Moriarity, 2014), and

attitudes (Killian & Bastas, 2015; Wu & Hwang, 2010). Transactional distance has been

examined using a variety of measures within various learning settings, including

computer-aided instruction (Kickul & Kickul, 2006), game-based learning (Bauer et al.,

2012), hybrid learning structures (Moffett & Mill, 2014; Velegol et al., 2015), face-to-

face (Al-Dujaily et al., 2013), and two-way video distance learning (Chen & Willits,

1998; Falloon, 2011). However, Bolliger and Erichsen (2013) recommended that as new

environmental conditions arise, those settings must also be explored. Such was the case

with asynchronous video e-learning. Personality traits had demonstrated associations

with the quality of learner interactions within the video environment, including two-way

video distance education (Barkhi & Brozovsky, 2003; Tsan & Day, 2007) and

asynchronous video discussion boards (Borup et al., 2013), but not within the

asynchronous video e-learning environment.

Having examined the relationship between learner personality traits and

transactional distance within the asynchronous video environment, this research added to

the literature regarding the personality construct-learning interaction relationship with the

goal that future researchers will seek to determine a theory that unifies self-regulatory

processes, learner outcomes, and learning environments. TDT describes the primary

factors for determining transactional distance as dialogue, learner autonomy, and

structure, which are constructs of the learning environment’s design (Park, 2011). The

present research highlighted the role of self-regulatory processes, such as personality

traits, upon transactional distance and emphasized the learner’s role in the two-way

interaction between the learner and the e-learning setting in lieu of focusing on the e-

learning environment exclusively.

Although understanding the relationship between learner personality traits and TD

with the learning environment filled a gap in scholarly research, the real-world

application of the information may be equally significant. As of 2012, the corporate e-

learning market in North America was valued at over $23.8 billion with projections for it

to rise to $27.1 billion by 2016 (Docebo, 2014). Additionally, the Docebo (2014) report

identified that video use, both synchronous and asynchronous, is the emerging trend

within the corporate e-learning space. Within the consumer market, demand exists for

distance learning focused on practical skills, with approximately 70% of the market

consisting of women, most of who are affluent and live on the East or West coasts of the

U.S. (LaRosa, 2013). Skills of interest include business-related skills, such as

communication, finance, and computer skills, while interpersonal skills, such as

relationship development, communication, and negotiation, also remain popular.

Although the problem statement applied to both the corporate and consumer markets, as

well as educational markets, the population of interest for the present study was the self-

improvement consumer market. By identifying more effective ways in which learners

can utilize asynchronous video learning, developers for e-learning providers can better

meet market demands of e-learning consumers, providing more satisfying learning

experiences for the customer and a stronger bottom line for the development company.

Purpose of the Study

The purpose of this quantitative method, correlational design study was to

examine the relationship between FFM personality traits and perceived transactional

distance for learners in an asynchronous video-based e-learning environment. The

personality traits were measured using the Big Five Inventory scale, which indicated the

strength of each participant’s personality traits (Benet-Martinez & John, 1998; John,

2009; John, Donahue, & Kentle, 1991; John, Naumann, & Soto, 2008). The second

variable, transactional distance, was measured using the Structure Component Evaluation

Tool (SCET), a transactional distance self-assessment survey instrument (Horzum, 2011;

Sandoe, 2005). The population of interest for this research was individuals within the

United States that participate in self-improvement e-learning courses. This population

includes individuals seeking e-learning content designed for personal improvement, skills

development, and individual enjoyment, and does not include formal education, such as

online universities or trade schools, and does not include corporate distance learning.

The research sought to address a gap in the literature identified by Bolliger and

Erichsen (2013) describing the relationship between personality traits and satisfying

interactions within different e-learning environments. A preponderance of research (e.g.,

Killian & Bastas, 2015; Lucas et al., 2013; Wu & Hwang, 2010) investigated the

relationships between various psychological constructs and learner interactions within

differing environments. However, the emerging technology of asynchronous video-based

e-learning had not been investigated with this study’s variables in mind. As a result, the

efforts of this study added to the landscape of research regarding learner interactions

within the online learning environment. Specifically, this research added to literature that

sought to correlate personality traits and transactional distance within specific learning

conditions with the end goal of maximizing positive learning outcomes. The present

research, for example, addressed the suggested research topic of investigating training

outcomes across a variety of learner control conditions based upon personality profiles

(Orvis et al., 2011). This study also extended Al-Dujaily et al. (2013) by examining the

role of personality within the e-learning environment using non-computer science

students. Using non-computer science students was a critical distinction, as computer

experience may mask the moderating effects of some personality traits within the online

environment and experience may contribute to improved learner performance in the

online environment beyond the effects of previous knowledge (Simmering, Posey, &

Piccoli, 2009).

The present research also directly addressed the gap in the research as identified

by Bolliger and Erichsen (2013), which recommended that future research investigate the

relationship between personality types and learner interaction satisfaction, which was

measured by transactional distance, within emerging settings. Lastly, the study described

a unique combination of TDT factors dialogue, learner autonomy, and structure,

providing the opportunity to examine the efficacy of TDT within emerging learning

structures (Chen & Willits, 1998). A unique facet of the asynchronous video format is

that perceived dialogue has been noted in non-learning environments between viewers

and on-screen actors, which contributes to viewer-perceived relationships with actors, a

phenomenon that was correlated with personality traits (Maltby, McCutcheon, &

Lowinger, 2011). This perceived dialogue, which correlated with trait Extroversion, is an

internal dialogue within the viewer that assists in creating a cognitive space in which a

relationship can exist. The accumulation of this and related research informs the

instructional design field, enabling the construction of e-learning architectures that adapt

to the learner’s needs based upon individual predispositions (Dominic & Francis, 2015).

More generally, the present research extended the role of self-regulatory processes, such

as personality traits, within Transactional Distance Theory, which focuses on design

elements of structure, designed dialogue paths, and permissible learner autonomy as

primary influencers of transactional distance (Park, 2011).

Research Questions and Hypotheses

Scholarly literature regarding the influence of personality traits on video viewing

or learning preferences was limited. Within video conferencing environments, MBTI

type Feeling (Barkhi & Brozovsky, 2003), which most closely correlates to FFM trait

Agreeableness (Furnham, Moutafi, & Crump, 2003), was related to increased individual

communication satisfaction. Higher levels of trait Extroversion were related to improved

trust and more positive attitudes in two-way video counseling (Tsan & Day, 2007). In

contrast, high levels of Extroversion were related to lower student participation patterns

in asynchronous video communications (Borup et al., 2013). Additionally, trait

Extroversion has been positively related to perceived relationship development with on-

screen actors in non-learning environments (Maltby et al., 2011). As a result, this study

focused on the potential relationships between personality traits and interaction

satisfaction, as described by transactional distance theory and measured by the Structure

Component Evaluation Tool (Sandoe, 2005), within the asynchronous video e-learning

environment. Each of the personality traits represented a research variable, the strength

of which was measured for each participant using the Big Five Inventory (John, 2009)

scale before their participation in a 30-minute e-course module on communication in

relationships. Participants then completed the SCET (Sandoe, 2005), which measured

their perception of transactional distance during the e-course. Personality trait data was

analyzed for its relationship to the participant’s perception of TD. A comparison of each

personality trait variable to the transactional distance variable addressed the problem of

determining if there was a relationship between the two variables, and, if so, to what

degree the relationship existed. SCET values are inversely related to transactional

distance in which a high SCET value represents a small TD and a low SCET value

represents a wide TD. The following research questions and hypotheses guided this

research study based upon the listed variables:

V1: FFM personality traits as measured by the Big Five Inventory (John, 2009)

• V1O: FFM personality trait Openness as measured by the Big Five Inventory (John, 2009).

• V1C: FFM personality trait Conscientiousness as measured by the Big Five Inventory (John, 2009).

• V1E: FFM personality trait Extroversion as measured by the Big Five Inventory (John, 2009).

• V1A: FFM personality trait Agreeableness as measured by the Big Five Inventory (John, 2009).

• V1N: FFM personality trait Neuroticism as measured by the Big Five Inventory (John, 2009).

V2: Transactional distance as measured by the Structure Component Evaluation

Tool (Sandoe, 2005)

RQ1: Is there a significant correlation between Five-Factor Model personality traits

and transactional distance within the asynchronous video-based e-learning

environment?

H1A-O: Trait Openness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-O: Trait Openness does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

H1A-C: Trait Conscientiousness correlates significantly with transactional distance in

the asynchronous video-based e-learning environment.

H10-C: Trait Conscientiousness does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

H1A-E: Trait Extroversion correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-E: Trait Extroversion does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-A: Trait Agreeableness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-A: Trait Agreeableness does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-N: Trait Neuroticism correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-N: Trait Neuroticism does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning

environment?

H2A-O: Trait Openness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-O: Trait Openness is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-C: Trait Conscientiousness is significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H20-C: Trait Conscientiousness is not significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

H2A-E: Trait Extroversion is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-E: Trait Extroversion is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-A: Trait Agreeableness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-A: Trait Agreeableness is not significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H2A-N: Trait Neuroticism is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-N: Trait Neuroticism is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

Within the study, a significant positive or negative correlation between a

personality trait with transactional distance and a statistically significant degree of

prediction supported the associated alternative hypothesis and rejected the null

hypothesis. Additionally, and more meaningfully, such results addressed the gap in the

research as identified by the problem statement by describing the relationship between

the personality trait and learner perceived transactional distance.

Advancing Scientific Knowledge

The existing research was limited in its exploration of the influence of personality

traits on learner behaviors and outcomes within the asynchronous video e-learning

environment. A trend in e-learning research was investigating learner outcomes as it

related to the learner’s psychological constructs. A majority of research in active

learning indicated that the greater the amount of learner activity, the better the learner

performs (Lucas et al., 2013). However, not all learners in face-to-face environments

engaged with the activity in the same manner or with the same level of attention,

differences that may be explained by the psychological constructs of self-efficacy

(Hauser et al., 2012), motivation (Byun, 2014), and attitudes (Wu & Hwang, 2010).

Further investigation suggested that learner personality traits might be the underlying

construct (Donche et al., 2013; Kim, 2013).

Research in the online environment experienced a similar path, with research

examining learner outcomes within differing environments. The results indicated that

psychological constructs appeared to correlate with the level of learner satisfaction and

performance based upon the environmental conditions, such as the structure, availability

to communicate, the boundaries set on the learner, and the learner’s behavior (Falloon,

2011). The research examined personality traits as a correlate to learner behavior within

e-learning environments as measured by the self-reported strength of the learner’s

interaction with the instructional source within variety of e-learning environments,

including computer-aided instruction (Kickul & Kickul, 2006), hybrid online and in-class

environments (Al-Dujaily et al., 2013), and game-based learning (Bauer et al., 2012).

However, the developing e-learning environment of asynchronous video instruction had

not yet been explored, thereby creating a gap in the research.

These investigations were supported by personality trait theory, which suggested

that individuals’ personalities are composed of hundreds of facets, which are clustered

into major categories. A widely accepted personality trait model is the Five-Factor

Model, which offers five broad traits of human behavior: Extroversion, Neuroticism,

Openness to Experience, Agreeableness, and Conscientiousness (McCrae & Costa,

2003). Individual personality traits are considered stable over time and personality traits

moderate behavior such that individual tendencies within environments are consistent

over time (Wortman et al., 2012).

Within the online environment, the Theory of Transactional Distance assists in

describing the relationships between learner, the instructor, and learner outcomes (Moore,

1993). TDT offers that the interaction between a learner and the instructor is influenced

by three factors: dialogue, the learning structure, and the amount of learner autonomy.

The amount of perceived pedagogical distance between the learner and the instructor is

called transactional distance. The closer the TD, the more able the learner is to ask

questions, clarify information, and engage in learning activities, which, in turn, supports

higher learning performance (Hauser et al., 2012).

Falloon (2011) recommended exploration of the efficacy of the virtual classroom

while considering individual preferences within various environments, a call that has

been answered for a variety of environments, including hybrid online and in-seat

classrooms (Al-Dujaily et al., 2013; Murphy & Rodríguez-Manzanares, 2008),

asynchronous computer-assisted instruction (Kickul & Kickul, 2006), and game-based

learning (Bauer et al., 2012; Mayer, Kortmann, Wenzler, Wetters, & Spaans, 2014).

Bolliger and Erichsen (2013) furthered the call to specifically examine the correlation

between personality types and satisfying interactions within different learning

environments. The present study measured personality traits of the sample population

and compared those measures to the participants' perceived TD within the asynchronous

video environment. The research determined whether or not a relationship exists

between FFM personality traits with learner behavior within the prescribed learning

structure. The immediate results of this study specifically addressed the gap identified by

Bolliger and Erichsen (2013), and advanced scientific knowledge about the relationship

between personality traits and TD within the video e-learning environment, an

environment that had heretofore not been explored.

The present study provided insight into Moore’s (1993) construct of dialogue,

which Moore defines as interaction that is “purposeful, constructive, and valued by each

party” (p. 24). Although dialogue has traditionally been thought of as a series of real

interactions, the asynchronous video environment presents the opportunity for perceived

dialogue between the viewer and the actor, a phenomenon known to occur between fans

and celebrities in which a unidirectional attachment develops, creating a value to the

viewer and sense of interaction between the two as perceived by the viewer (Maltby et

al., 2011). The result of the perceived dialogue is a smaller transactional distance.

Although TDT has transactional distance at the center construct of the theory (Gibson,

2003), Moore also addresses the learner’s characteristics as being salient to the equation.

Moore (1993) emphasized that TD is a relative variable influenced by the learner’s

behaviors and characteristics, amongst other factors. The present study further defined

Moore’s construct of the learner to include self-regulatory processes, such as specific

personality traits, as relevant to individual learning interactions.

The results also provided discussion points regarding personality trait theory.

With a correlation between personality traits and transactional distance, personality

theorists could more fully define the personality trait to include preferences and behaviors

within distant or electronic environments. For example, if Extroversion was correlated

with improved interaction within the asynchronous video environment, which was a

measure of the present study, as well as being correlated to procedural knowledge in an

adaptive environment (Al-Dujaily et al., 2013), being positively correlated with high

learner control environments (Orvis et al., 2011), related to increased trust within video

environments (Tsan & Day, 2007), and related to decreased participation on

asynchronous video discussion boards (Borup et al., 2013), personality theorists could

seek commonalities suitable for enhancing the definition of the trait.

Significance of the Study

The literature demonstrated a relationship between personality traits and

transactional distance within a variety of environments, including computer-aided

instruction (Kickul & Kickul, 2006), blended online and face-to-face (Al-Dujaily et al.,

2013), game-based learning (Bauer et al., 2012), and autonomous learning conditions

(Orvis et al., 2011). The compilation of literature allows for the mapping of personality

traits to environments in which the learner produces the most desirable outcomes. The

present research added additional structure to the interaction map for video-based e-

learning. Once developed, the map of relationships between personality traits and

learning environments will inform studies searching to develop theories relating

personality constructs, including FFM personality traits, and learning environments. The

development of such theories will enable researchers and instructional designers the

ability to predict behaviors within future e-learning environments.

For the present time, determining the relationship between personality traits and

transactional distance within the video e-learning environment expanded the scholarly

literature of individual traits and their influence on e-learning. Practical applications of

the research results include equipping instructional designers with an extended catalogue

of learning frameworks that includes asynchronous video e-learning and its association

with personality traits for maximizing individual learner outcomes (Benson &

Samarawickrema, 2009; Hwang et al., 2013). Real-world applications included user-

selected learning frameworks based upon learner preferences (Fraihat & Shambour,

2015), and adaptive learning applications (Takeuchi et al., 2009).

Additionally, correlations between learner personality traits and transactional

distance within the video environment provided information beneficial for the design,

development, and implementation of other online video forums, such as social

environments in which trust development is important (Zhao, Ha, & Widdows, 2013),

collaboration within virtual teams (Dullemond, van Gameren, & van Solingen, 2014),

and distant healthcare and social services (Weber, Geigle, & Barkdull, 2014). The

application of trait-interaction information within the video environment extends to any

situation in which video, either synchronous or asynchronous, is practiced. Seemingly

minor applications include understanding the efficacy of video instruction for providing

passenger pre-takeoff instructions for airlines, safety briefings for utility workers, and

organizing large workgroups. Although these purposes may not seem to be related to the

e-learning environment, any social interaction, real or perceived, provides a learning

opportunity (Bandura, 1977; Mintzes, Marcum, Messerschmidt-Yates, & Mark, 2013).

Theoretical insights also emerged from this research. The results helped to

determine whether Agreeableness interacted with the video environment due to a

perceived relationship with the on-screen instructor. Agreeableness is associated with

characteristics of pleasing and accommodating (McCrae & Costa, 2003), which may be

related to weak internal motivations based upon others’ expectations (Briki et al., 2015;

Deci & Ryan, 2008). A correlation between Extroversion and learning behavior within

the asynchronous video environment provided additional support for an incentive-based

motivation model for Extroversion. Incentive-based models of motivation state that an

individual becomes motivated by the anticipation of rewarding activity, such as

answering questions correctly and demonstrating knowledge before an audience—in the

case of the present research, the perceived audience of the video instructor (Merrick &

Shafi, 2013). Trait Extroversion also correlated with Entertainment-social scores of

celebrity worship, a phenomenon associated with asynchronous video in which the

viewer develops a perceived attachment and strong interest in the on-screen actor (Maltby

et al., 2011), a construct that might have influenced the characteristic of dialogue within

the asynchronous video e-learning environment and one that might suggest a need to

expand the definition of dialogue to include perceived dialogue as a factor of

transactional distance. Such a construct would be supported by Theory of Mind precepts,

as an internal dialogue exists between the individual and the perceived mind of the other

in order to establish communication and to create a cognitive space for the other persona

(Harbers, Van den Bosch, & Meyer, 2012).

Rationale for Methodology

Research of personality typically follows one of three avenues: the examination of

individual differences, the examination of motivations, or holistic examination of the

individual (McAdams & Pals, 2007). The study of individual differences is based upon

trait study, which is a lexical categorization based upon factor analysis of the words’

applicability to individual tendencies (John & Srivastava, 1999). As a result, it is

appropriate to use quantitative methods to study traits, the categorization of which was

born of quantitative methods. Quantitative methods emerge from positivism, the concept

that every problem has a solution and that there is an interrelated cause and effect that can

be measured (Arghode, 2012). The governing epistemology of positivism is one in

which the detached observer seeks out a singular truth through cause and effect, or

through correlation and association, which was of interest to this study. The resulting

methodology analyzes the assumptions, principles, and procedures to seek out the

relationship of interest. Consequently, quantitative methods are appropriate for the

development and testing of hypotheses (Dobrovolny & Fuentes, 2008), for measuring

differences between variables and determining relationships between variables, and for

exploring phenomenon that are repeatable (Arghode, 2012).

Quantitative methods also provide a fixed standard against which the theory,

research question, hypotheses, and variables are measured and compared, providing a

series of theoretical and procedural benchmarks against which all similar research is

contrasted (Wallis, 2015). The nature of quantitative methods offers structure within

which the data is assembled for examination in an objective manner that is acceptable to

the research community. Such methodology contrasts with qualitative methodology,

which seeks to develop theory based upon an interpretation by an involved observer of

the phenomenon (Arghode, 2012).

The current study’s purpose was to measure the strength of the relationship

between each personality trait’s effect and transactional distance within the learning

environment, which suggested that the research utilize quantitative methodology. Several

characteristics of personality traits influenced methodology selection: Individual trait

dispositions were testable, the measurement of personality traits produced a value along a

continuous scale, and, although personality traits cannot be manipulated, sufficient

samples were taken to create a quasi-experimental approach. Instruments, such as the

Big Five Inventory (John, 2009), Myers Briggs Type Indicator (Furnham et al., 2003),

Trait Descriptive Adjectives (John & Srivastava, 1999), Saucier’s Mini-Markers (Dwight,

Cummings, & Glenar, 1998), and the revised NEO personality inventory (NEO-PI-R)

(Costa & McCrae, 1995) have been developed to measure the strength of personality

traits along each instrument’s respective axes. Previous research has shown that

transactional distance, which is measured using quantitative surveys (Chen, 2001;

Horzum, 2011; Huang, 2002; Sandoe, 2005), changes based upon differences in the

personality variable following experience within a specific learning environment (Al-

Dujaily et al., 2013; Bauer et al., 2012; Kickul & Kickul, 2006; Orvis et al., 2011). Each

of these characteristics fit the definition of a variable.

Quantitative research investigates psychological constructs through statistical

means. The design most suited to address the research questions and hypotheses for the

selected environment was correlational design (Jamison & Schuttler, 2015; Rumrill,

2004). Quantitative methodology and correlational design afforded the research the

opportunity to maintain an objective view and minimize observer bias (Trofimova, 2014),

while enumerating the strength of the relationship between the two variables.

Quantitative methods also afforded future researchers the opportunity to verify, enhance,

and expand the current research. Quantitative methods do not discover new variables as

qualitative methods would discover factors, nor do quantitative methods describe a

situation globally or holistically. Quantitative methods are limited to answering the

specific question around which the research was designed, which is demonstrated through

similar research including Kim (2013) and Bolliger and Erichsen (2013).

Nature of the Research Design for the Study

This study used a correlational design. The correlational design offered the

benefit of identifying associative relationships between variables and allowed the

researcher to measure relationship strength (Rumrill, 2004). Data collected from a

correlational study must meet the criteria that measurements of the variables must be

continuous in nature, which is true of FFM traits (John et al., 2008); and TD

measurements from the Structured Component Evaluation Tool (Sandoe, 2005).

Correlational design is also useful for non-experimental or quasi-experimental

environments in which the variables cannot be manipulated or controlled (Jamison &

Schuttler, 2015; Rumrill, 2004), which was the case with personality traits in this study.

It is also important to note that correlational designs do not attempt to identify causal

relationships; however, covariation is a necessary condition for causality.

The personality variables were FFM personality traits Openness,

Conscientiousness, Extroversion, Agreeableness, and Neuroticism, each of which was

investigated independently in relation to the learning outcome variable. These traits were

selected for examination based upon previous associations of personality traits with

learner interaction within the video environment, including two-way video distance

education (Barkhi & Brozovsky, 2003; Tsan & Day, 2007) and asynchronous video

discussion boards (Borup et al., 2013). Personality traits were measured using the Big

Five Inventory, which assigned a score for each trait, which was normalized to a range

from 0 to 100, with 50 representing the midpoint (John, 2009). Scores higher than the

midpoint represent the high dimension of the trait (e.g., extroversion), while scores lower

than the midpoint represent the lower dimensional trait (e.g., introversion). The bipolar

nature of each dimension puts forth that the further the score is from the midpoint, the

stronger the expression of that dimension. The present research design was based upon

Kim (2013) in which the researcher examined the relationship between personality traits

and academic outcomes, as well as the relationship between Kolb learning styles and

academic outcomes, following the learner’s completion of a blended e-learning and in-

class communications course.

The learning outcome variable was transactional distance, which represented the

perceived strength of the interaction between the learner and the learning environment.

TD is measured using the Structured Component Evaluation Tool (SCET) (Sandoe,

2005). SCET was developed to measure TD within e-learning environments that exhibit

high levels of structure, which was the case with an asynchronous e-learning

environment. SCET scores range from 0, which represents no perceived learner-

instructor pedagogical relationship, to 24, which represents a very strong learner-learning

environment relationship.

The design facilitated Pearson correlation analysis to determine whether any

personality variable exhibited a significant relationship with TD. Pearson correlation

analysis was the most suitable method as it was reliable for bivariate correlation of

continuous variables in linear relationships. Studies similar to the present research (e.g.,

Caprara et al., 2011; Kamaluddin, Shariff, Othman, Ismail, & Saat, 2014) successfully

used a Pearson correlation. Results from the Pearson correlational analysis addressed the

hypotheses, with significant results affirming the alternative hypotheses (Kim, 2013).

Additionally, correlational design offered the benefit of comparing variables over which

the experimenter had no control (Rumrill, 2004), which was the case with personality and

learning outcome variables. Because the variables were unable to be experimentally

manipulated, experimental designs were inappropriate. In the unlikely event that one of

the variables was determined to be non-continuous, or if significant outliers were present,

Spearman correlation analysis would have been used, as it is a method suitable for

continuous and ordinal data sets, and an analysis better suited to address outlier data sets

(Gravetter & Wallnau, 2013).

The design also employed an analysis of regression, which measured the ability of

the personality traits to predict learners’ ratings of transactional distance. Data for

analysis of regression assumes the data is linear, normally distributed, homoscedastic, the

variables are not auto-correlated, and the data is not collinear (Meyers, Gamst, &

Guarino, 2013). Personality trait measures, as determined by BFI results, were compared

to transactional distance measures, as described by SCET results. Each trait was

independently compared to determine the extent of the variance of TD as explained by

the personality trait. Significant results (p < .05) rejected the null hypothesis, and non-

significant results fail to reject the null hypothesis. A positive correlation between a

personality trait and SCET values represent a negative correlation between the

personality trait and TD, since high SCET values represent small transactional distances

and low SCET values represent large transactional distances. Personality trait-based

research utilized analysis of regression to determine the degree to which personality traits

explained the outcome variable across the literature, to include Jong (2013), Saricaoglu

and Arslan (2013), and Kim (2013).

The target population was a subset of the commercial e-learning market. The

$23.8 billion e-learning market in North America is projected to rise to $27.1 billion by

2016 (Docebo, 2014), and video use, both synchronous and asynchronous, is anticipated

to be the emerging trend within the e-learning space. The consumer e-learning market, of

which 70% are women, the majority of who live on the East or West U.S. coasts, and

who are affluent, is focused on practical skills (LaRosa, 2013). This market typically

accesses learning from home and is interested in self-improvement through courses

focused on business-related skills, such as communication, finance, and computer skills,

and interpersonal skills, such as relationship development, communication, and

negotiation. Thus, the sample for the present study was participants in self-improvement

e-learning courses. Using a bivariate normal model approach for correlation, the

G*Power 3.1 software program calculated that a minimum of 84 data sets were necessary

for this study to achieve a power of .80 and a maximum error probability of .05 based

upon an anticipated moderate correlation (r2 = .3) and a two-tailed test based upon a

general population of greater than 10,000 (N < 10,000) (Orvis et al., 2011; Peng, Long, &

Abaci, 2012).

Participants were recruited via advertising methods. Direct mail postcards and

Internet advertising were employed seeking individuals 18-years of age or older

interested in taking a free online course on the topic of communication skills for

relationships for this convenience sample. In order to maximize the advertising

opportunities, marketing targeted individuals in a relationship so that the e-learning

course, communication within relationships, was relevant to the participant. Direct mail

mailing lists targeted suburban single-family home communities in the Phoenix

metropolitan area, where 73% of single-family homes are purchased by either married or

unmarried couples (Snowden, 2015). Internet advertising utilized keywords marriage,

relationship, marriage courses, free online communication courses, and marriage courses

online, within major search engines (Google, n.d.). Advertising for participants was

ongoing and continued for the time necessary to collect the required minimum sample

size of completed data sets. This approach addressed the need to ensure a qualified

sample population, as well as to address attrition. Individuals interested in participating

were provided a web link via the advertising material to the research study website at

which point the participant was presented with a video introduction to the study. A video

then described the Informed Consent Form (see Appendix B), which was presented for

review and electronic signature. The next video segment asked participants to complete a

brief demographic survey and an online version of the Big Five Inventory (John, 2009).

Upon completion of the pre-course form and instrument, participants in the

proposed research study experienced an independent e-learning course delivered via

asynchronous video instruction. The course featured three modules, each of which began

with a slide showing the module objectives, followed by a five- to seven-minute video

discussing a facet of interpersonal communication. Within each module, the video

instructor directly addressed the camera as if speaking directly to the individual learner,

and did so using casual conversation and personal anecdotes, which has been shown to

develop a stronger rapport with online video learners (Kim & Thayne, 2015). Each

module provided interactivity through two multiple-choice questions based upon the

learning objectives. As a non-credit course, response accuracy bore no influence on the

participant’s completion of the course, although participants received prompts for

incorrect answers and were offered the opportunity to reattempt answering the question.

Each participant experienced the same three-module course and the course provides no

opportunities for learner interaction with the instructor or other learners. The

transactional distance factors that describe this asynchronous video course were high

structure due to the rigidity of the course flow (Park, 2011), low learner autonomy with

learners having little freedom to explore information outside of the course, which is a

function of the high structure (Benson & Samarawickrema, 2009), and low dialogue with

learners having no opportunity to ask questions or clarify concepts with an instructor or

peers (Moore, 1989; Park, 2011).

Following the third module, participants viewed video instructions for completing

the Structure Component Evaluation Tool (Sandoe, 2005) and then were presented with

the SCET instrument. Upon completion of the SCET, a short video played thanking the

participant for his or her involvement with the research and a brief summary of the study.

The video provided the participant with contact information in the event he or she would

like follow-up communication with the researcher.

The design included inherent risks. The distribution of personality traits may not

have been normal, producing a restricted range of data, and validity may have been

questioned due to potential covariance between the personality variables (Levy & Ellis,

2011). Such covariance would have been examined via analysis of covariance

(ANCOVA) provided the data meets the assumptions of linearity of regression and

homogeneity of regression (Meyers et al., 2013). These risks were mitigated through an

appropriate sample size calculated to match the design, including number of variables,

effect size, statistical analysis method (Gravetter & Wallnau, 2013), and by selecting a

diverse sample population (Al-Dujaily et al., 2013).

Definition of Terms

Using clear and unequivocal definitions is important for unambiguous

understanding of terms and constructs used within a study (Howards, Schisterman, Poole,

Kaufman, & Weinberg, 2012). The following terms are defined to afford a common and

clear understanding for the purposes of this study. The order in which the terms are

presented is intended to allow the reader to understand and define terms beginning with

broad concepts and then to focus upon specific constructs within each significant area of

study.

For the purpose of this study, the following terms are defined as follows:

Personality trait. The grouped collection of behavioral descriptors that is

taxonomically interrelated (McCrae & Costa, 2003). There are five such groupings per

the Five-Factor Model, which include Extroversion, Agreeableness, Conscientiousness,

Openness to Experience, and Neuroticism.

Big Five. The Big Five is a reference to the five personality traits clusters

evolving from the work of Tupes and Christal (1992). The Big Five traits are

Extroversion, Agreeableness, Conscientiousness, Openness to Experience, and

Neuroticism.

Five-Factor Model. An integrated taxonomy of the Big Five personality traits

developed by McCrae and Costa (2003) to provide a unified model of personality. Five-

Factor Model (FFM) suggests that personality traits do not change significantly over the

course of an individual’s life and they are useful for predicting individual tendencies in

known circumstances (Wortman et al., 2012)

Openness to Experience. Also known as Openness. Behavioral characteristics

and descriptors related to an individual’s tendencies for valuing individual expression and

for exhibiting intellectual curiosity. Facet descriptors include idealism, intellectualism,

and adventurousness (Soto & John, 2012). Individuals high in Openness are interested in

others’ opinions, even if they initially disagree, and are willing to change their mind

based upon the evidence presented.

Conscientiousness. Behavioral characteristics and descriptors related to an

individual’s tendencies to organize and stay focused on tasks. Descriptive facets include

industriousness, orderliness, self-discipline, moral seriousness, work ethic, and focus on

long-term goals (Soto & John, 2012). Individuals high in Conscientiousness are

organized, with neat desks, files in order, and goals set for their day.

Extroversion. Also known as Extraversion. Behavioral characteristics and

descriptors related to an individual’s tendencies within social interactions and to their

sense of agency (Klimstra, Luyckx, Goossens, Teppers, & De Fruyt, 2013). Extroversion

describes the level of individual assertiveness, social confidence, and gregariousness

(Soto & John, 2012). An example of an individual with Extroversion is one who is

comfortable socializing with everybody in attendance at a party, while someone who is

low in Extroversion would be more comfortable talking with the same, familiar person all

evening, or retreating to a quiet location with no one around.

Agreeableness. Behavioral characteristics and descriptors related to an

individual’s tendencies for straightforwardness and modesty (Klimstra et al., 2013).

Descriptive facets include trustfulness, compassion, and humility (Soto & John, 2012).

Characteristics of an individual with high Agreeableness tendencies is one who attempts

to please those around, such as not sending back an undercooked steak at a restaurant. A

person low in Agreeableness would, on the other hand, send the steak back and ask for a

free appetizer. Agreeableness includes a sense of caring how others consider the

individual.

Neuroticism. Behavioral characteristics and descriptors related to an individual’s

tendencies to feel negative affect, such as to feel nervousness, fear, or sadness. High

neuroticism is susceptible to intrusive thoughts and behaviors, and is described with

descriptors such as anxiety, depression, rumination, and irritability (Soto & John, 2012).

Individuals high in Neuroticism tend to display nervous or stressful behaviors, even if the

situation does not merit higher levels of affective arousal.

Bipolar. Representing two ends of the same personality trait scale. Each

personality characteristic (e.g., Extroversion) may exhibit one tendency of a trait to some

extent, such as gregariousness, or it may exhibit an opposite tendency of the trait to some

extent, such as shyness (McCrae & Costa, 2003). This use of this term should not be

confused with bipolar disorder, describing manic episodes of mood disturbances

(American Psychiatric, 2013).

Transactional distance. Transactional distance, or TD, is the perceived

pedagogical distance between a learner and the learning environment (Park, 2011). TD is

a result of the psychological and communication closeness that the learner experiences

with the instructional source. A high TD refers to a lack of communication or

understanding between the learner and instructor, and a low TD refers to an intellectual

and affective closeness between the learner and instructor. Low TD is associated with

improved learner performance (Hauser et al., 2012). TD is determined by factors of

dialogue, structure, and learner autonomy.

Interaction. Interaction is the interplay of and satisfaction with knowledge,

affect, and behaviors between the learner and the learning environment (Mason, 2013).

The quality and intensity of an interaction within the distance-learning environment is

measured as transactional distance (Ustati & Hassan, 2013).

Dialogue. Dialogue describes the broad spectrum of purposeful, positive, and

synergistic interaction between the learner and the instructor (Moore, 1993). Dialogue

connotes the idea of multi-directional communication for the purpose of clarifying,

understanding, and furthering the learning of the student. Dialogue does not include the

act of programmed content delivery.

Structure. Structure refers to instructional design by which the curriculum is

delivered to the learner via the prescribed communication medium (Moore, 1993).

Concepts, such as the flexibility of the instructional design to adjust to the learner’s needs

and the ability for the technology to accommodate the instructional design, are included

within the structural taxonomy, as are pedagogical considerations of educational

objectives, learning content, assessment activities, and addressing student motivation

(Benson & Samarawickrema, 2009).

Learner autonomy. Learner autonomy addresses two principle concepts within

the learning environment. The first is the amount of flexibility a learner is provided by

the learning structure to determine learning objectives, create knowledge, and achieve

goals (Moore, 1993). The second concept of learner autonomy includes the

psychological view of a learner’s willingness or ability to be self-directed (Park, 2011).

Learning environment. Learners may engage in up to four different types of

interactions within the distance-learning environment in order to acquire knowledge.

Engagement may occur between a learner and an instructor, between learning peers,

between a learner and the content, such as the text or video providing information

(Moore, 1993), and between a learner and the interface through which the learner

accesses the instruction (Chen, 2001). The learning environment encompasses all four

types of engagement. Most TDT concepts apply consistently to all learner-learning

environment interactions. For those cases in which a broad application does not apply,

the specific interaction type (e.g., learner-content) is identified.

Asynchronous video-based e-learning. Asynchronous video-based e-learning

refers to the learning environment in which video content is presented to the learner at the

learner’s convenience, including the factors of time scheduling and Internet-connected

device, such as laptop or mobile device. This learning environment is delivered via the

Internet and typically includes interactive activities, such as assessments, unstructured

research, and related discussion boards (Stigler et al., 2015). This learning environment

compares to computer-aided instruction, except that the primary media for content

delivery is video instead of text, for a richer form of media presentation (Ljubojevic et al.,

2014).

Assumptions, Limitations, Delimitations

Assumptions, limitations, and delimitations of the research provided

epistemological boundaries in order to support the internal and external validity of

research (Ellis & Levy, 2009). By stating the restrictions a priori, readers are better able

to understand the viewpoint of the researcher and limit the challenges to the research

methodology. Assumptions represented the values and epistemological positions of the

researcher and affected how the research was conducted (Kirkwood & Price, 2013).

Limitations were potential problems or weaknesses as identified a priori by the

researcher, and represented an uncontained threat to the to the internal validity of the

study (Ellis & Levy, 2009). On the other hand, delimitations represented actions, factors,

or variables left out of the research, resulting in a narrower investigation of the research

question (Ellis & Levy, 2009; Gallarza, Gil-Saura, & Holbrook, 2011).

This study relied upon several assumptions. These assumptions were:

1. The sample population represented the general population. By using direct mail and online advertisements to attract the sample population, it was possible that the sample might display psychological characteristics, such as motivation, that were slightly different than the general population. However, it was assumed that any individual that was seeking a course on communication skill for relationships was motivated by the content and not by the opportunity to participate in a research study.

2. It was assumed that participants connected to the research website using a high- quality Internet connection in order to receive the video content as it was intended to be delivered. While the study instructions recommended a high-speed connection, it was impossible to ensure this was the case.

3. It was assumed that study participants answered the survey questions honestly and that participants were not deceptive in their responses. Peter and Valkenburg (2011) found that given the appropriate introduction, survey participants provide honest answers instead of socially acceptable answers. For the purposes of this research, a video narrator asked participants to complete the instruments according to their experiences. In reference to SCET, the video stated that some learners felt that the video environment provided a high level of instruction or interaction while others felt the video environment provided a low level of instruction or interaction. Providing this information informed participants that there was not a socially correct answer.

4. The Five-Factor Model included descriptors for all normal human behavior. There is some disagreement that FFM includes all personality constructs. It has been argued that facets of honesty, humility, integrity, and greed are not included

within FFM (Thalmayer, Saucier, & Eigenhuis, 2011), while others suggested these elements are included within Agreeableness and Conscientiousness (McCrae & Costa, 2003). It was assumed within this research that those elements that may influence learner behaviors within the asynchronous environment were included within the FFM traits, and that any facets excluded by FFM did not have any bearing on the results (e.g., greed did not influence a learner’s interaction with the content). If any relevant facets of personality were excluded by FFM, those exclusions limit this study.

The study faced several limitations and delimitations.

1. A limitation of the study was that because the advertisement reached a national audience, it was not anticipated that a geographically-oriented population represented a majority of participants; however, it was not possible to predict the demographics of participants.

2. A limitation of the study was that there was no way to ensure that a normal distribution of personality traits was represented within the survey. In the event of a non-normal distribution based upon national surveys of personality distribution, such as described by Soto and John (2012), analysis would have included non-parametric statistical analysis.

3. A delimitation of the study was that the Structure Component Evaluation Tool was selected due to the structured nature of the video environment. Although the SCET is a validated and reliable instrument (Sandoe, 2005), it is possible that other tests for transactional distance may have returned different results based upon each test’s unique focus. This difference may affect generalizability of the results.

4. A delimitation of the study was that the study was examining FFM personality traits. It is possible that other psychological constructs, including motivation, attitudes, and self-efficacy, have a correlational relationship with the learner- learning environment interaction; however, these traits and constructs were not tested within this study, thus limiting the ability to generalize the results for all self-regulatory constructs.

Summary and Organization of the Remainder of the Study

A review of the extant literature found that potential correlations between

personality traits and transactional distance had not been investigated within the

asynchronous video e-learning environment; Bolliger and Erichsen (2013) identified a

gap in the research and recommended investigation of personality traits and learner

interactions within technologically diverse online and blended environments. Some

studies investigated the relationship between personality traits and transactional distance

in environments such as computer-aided instruction (Kickul & Kickul, 2006), hybrid

online and in-seat classes (Al-Dujaily et al., 2013), high and low learner autonomy online

environments (Orvis et al., 2011), and game-based learning environments (Bauer et al.,

2012). The studies found that personality traits correlated with transactional distance;

however, different traits influenced transactional distance dependent upon the unique

learning environment, differences that may be explained by the differing levels of

dialogue, structure, and learner autonomy available to learners within each environment.

Other studies investigated elements of video-based communication, including the

face-to-face classroom (Ljubojevic et al., 2014), two-way videoconferencing classrooms

(Chen & Willits, 1998), and blended environments, such as flipped classrooms (Moffett

& Mill, 2014; Velegol et al., 2015), to determine the influence of video upon

performance. Similar to the results found for the online learning environment studies, the

unique characteristics of the video environment appeared to influence outcomes, such as

satisfaction and academic performance. Only recently had asynchronous video-based e-

learning begun to receive attention. Vural (2013) investigated an asynchronous learning

environment to determine if active learning correlated with academic performance. In

the few studies relating personality traits and video environments, trait Agreeableness

was associated with individual communication satisfaction within two-way

videoconferencing environments (Barkhi & Brozovsky, 2003; Furnham et al., 2003), and

trait Extroversion was related to student participation patterns in asynchronous video

communications (Borup et al., 2013), and was related to trust and smaller psychological

distances in two-way counseling (Tsan & Day, 2007).

In order to add to the scientific literature, this study investigated the correlation of

personality traits with transactional distance within the asynchronous video e-learning

environment, and the extent to which the relationships predicted transactional distance.

Respondents to direct mail and online advertisements for an online course covering

communication skills for relationships were asked to participate in the online study, with

a minimum of 84 necessary to complete the study. The participants were asked to

provide demographic information (e.g., age, gender, average time each week spent using

a computer and the Internet), complete the Big Five Inventory, complete the

communications course, and complete the SCET. The data was screened and validated,

and imputation methods and pairwise deletion was used for missing data. Pearson

correlational analysis checked for significant relationships between the variables and

analysis of regression explained the degree of variance. Significant relationships

supported the alternative hypotheses and rejected the null hypotheses, and non-significant

relationships rejected the alternative hypotheses and accepted the null hypotheses.

The following chapter provides a development of personality trait theory and

transactional distance theory, and a thorough review of the extant literature on the topics

of constructivist learning, online learning, psychological construct correlations with

learning performance, personality trait correlations with learning performance, and the

evolution of video’s use for instruction. Next, the methodology chapter presents the

research design and describes the population, data collection, and data analysis process.

Chapter 4 presents the full implementation of the research, including the data screening,

testing of assumptions for statistical analysis, descriptive and inferential statistics, and the

results of the correlational analysis and analysis of regression. Chapter 5 discusses the

results through the lens of the research questions, relating the results to the previous

research and theories upon which the research was based, and discussing the implications

for future research and practice.

Chapter 2: Literature Review

Introduction to the Chapter and Background to the Problem

In order to describe the foundational factors that supported this investigation of

personality traits and their relationship with transactional distance in a video e-learning

environment, this chapter examines current and historical research on several important

concepts. The review of the literature surveyed peer reviewed journal articles and

dissertations found in the EBSCOhost search engine focusing on keywords of

personality, personality traits, Big Five, Five-Factor Model, Openness,

Conscientiousness, Extroversion, Agreeableness, Neuroticism, active learning, learning

style, e-learning, online education, distance learning, transactional distance, transaction,

video (not including games), self-esteem, self-efficacy, motivation, and satisfaction, as

well as books focused on these key areas. Literature searches focused on research

published in 2011 or later to ensure the inclusion of contemporary findings within the key

concept areas. The requirement for historical perspectives and concept development

supported using materials dated before 2011, particularly in the instances of theoretical

development, which leveraged the original research contained within seminal works. The

research attempted to demonstrate a combination of historical and contemporary research

to establish a path of related research and to expose areas requiring further investigation.

Overall, the chapter describes the background of the study, reviews the theoretical

foundations, and describes the extant literature to reveal the history, related theories, and

research accomplished on the topics relevant to the current study. The summary

concludes the chapter, exploring the gaps in the literature that led to the purpose of the

current study.

The chapter begins by discussing the theoretical foundations upon which the

balance of the research was conducted. Personality trait theory was developed so as to

provide understanding as to how traits describe an individual’s psychological construct

and why these constructs are useful for empirical research. Transactional Distance

Theory (Moore, 1993) was introduced to identify the e-learning components that

influence a learner’s transaction with the instructional source, which subsequently

influences learner outcomes. The first concept of constructivism was introduced in order

to lay a practical foundation for measuring learner interactions within the learning

environment (Ustati & Hassan, 2013). Within this section, research based upon active

learning (Lucas et al., 2013; Thomas & Macias-Moriarity, 2014) and Kolb’s Learning

Styles (Bhatti & Bart, 2013; Black & Kassaye, 2014; Chen et al., 2014) examined the

relationship between learners and their learning interactions as they influence

performance outcomes.

The next section explores the online learning environment in research that closely

mirrors the path of study taken by researchers of constructivism. Based upon

Transactional Distance Theory (Giossos, Koutsouba, Lionarakis, & Skavantzos, 2009;

Park, 2011), initial examinations of distance learning explored the types of relationships

and interactions that developed between learners and their instructors and peers (Chen,

2001; Moore, 1993). Early qualitative examinations of the distance-learning environment

identified learner psychological constructs and personality traits as influencers of the

learning process (Falloon, 2011; Murphy & Rodríguez-Manzanares, 2008). Subsequent

quantitative investigations explored individual factors of TDT. Specifically, the research

addressed the three components that make up transactional distance (TD), dialogue,

structure, and learner autonomy, and their influence on learner satisfaction and learner

academic performance (Benson & Samarawickrema, 2009; Hsia, Chang, & Tseng, 2014;

Islam, 2012; Papadopoulos & Dagdilelis, 2007; Wang & Morgan, 2008; Zhou, 2014).

The research broadened to include the effect of psychological constructs on each of the

transactional distance factors (Caprara et al., 2011; Hertel, Schroer, Batinic, & Naumann,

2008; Hetland et al., 2012; Wu & Hwang, 2010). With each individual transactional

distance factor thoroughly investigated, the research explored learner outcomes within

integrated learning settings, recognizing that dialogue, structure, and learner autonomy

vary dependent upon the unique learning environment (Hauser et al., 2012; Kim &

Thayne, 2015; Ljubojevic et al., 2014; Vural, 2013).

The research continued through the examination of psychological constructs and

personality traits as related to learner interaction within classroom environments and the

effects of those interactions on learning outcomes (Byun, 2014; Gosling, Augustine,

Vazire, Holtzman, & Gaddis, 2011; Killian & Bastas, 2015; Rodríguez Montequín et al.,

2013). Similar to the manner of the previous research, the literature review naturally

extended into the influence of psychological constructs and personality traits on learner

interactions within the e-learning environment by examining personality trait

relationships within a variety of learning environments, creating correlational ties

between traits and transactional distance as expressed within each unique learning

environment (Al-Dujaily et al., 2013; Bauer et al., 2012; Chang & Chang, 2012; Kickul

& Kickul, 2006; Orvis et al., 2011). As more data accumulated through the literature

describing the relationships between personality traits and learner interactions with the

different learning environments, it was expected that a pattern would emerge that allows

the development of theory to explain the relationships. In order to do so, additional

examination was needed of current and emerging learning settings (Benson &

Samarawickrema, 2009; Bolliger & Erichsen, 2013).

Throughout the preceding sections, a variety of learning environments were

explored, such as computer-aided instruction (Murphy & Rodríguez-Manzanares, 2008),

game-based learning (Bauer et al., 2012), and hybrid learning environments (Velegol et

al., 2015). However, research on the reemerging use of video within the online

environment was limited. The available research demonstrated the learning-applied uses

of video, including video as a support media within the face-to-face classroom

environment (Barkhi & Brozovsky, 2003; Ljubojevic et al., 2014), as a two-way

communication tool, such as videoconferencing (Falloon, 2011), and as a tool for hybrid

learning environments in which video provides the content to learners at home and then

the learners attend class to work on related activities (Moffett & Mill, 2014; Velegol et

al., 2015). The existing research of asynchronous video e-learning explored its

effectiveness through the lens of active learning (Vural, 2013). The following section

summarizes the key concepts of the literature review, identifying the gap that will be

investigated by the proposed study.

The literature was then used to examine the appropriate methodology for use in

the study. Quantitative methods were compared and contrasted with qualitative methods

to identify the benefits and shortcomings of each methodology in answering the research

questions. Qualitative methods reveal life stories of individuals and identify themes

associated with psychological constructs (Ma & Zi, 2015), while quantitative methods

provide methods for determining the strength of relationships between variables with the

literature that examines the relationships between personality traits and outcomes relying

primarily upon correlational design (Rumrill, 2004). The use of psychological constructs

such as personality traits as variables supported the use of correlational design, including

exploring relationships between Big Five traits and customer service job performance

(Blignaut & Ungerer, 2014), personality type and quality of life for cancer patients (Shun

et al., 2011), and psychological constructs of emotional intelligence, anxiety, stress, and

attitudes with learning outcomes (Opateye, 2014).

The chapter concludes with an examination of the instrumentation useful for

addressing the research questions. Personality traits may be examined using a variety of

measures, including revised NEO personality inventory (NEO PI-R) (Costa & McCrae,

1995), the Big Five Inventory (BFI) (Feldt, Lee, & Dew, 2014), and Saucier’s Mini-

Markers (Dwight et al., 1998). The literature examined each of the instruments by

identifying the instrument’s strengths and weaknesses, and comparing those attributes

against the study’s requirements in order to determine the most appropriate instrument for

this study, which is the BFI (Dwight et al., 1998; John & Srivastava, 1999).

Transactional distance measures were evaluated in a similar manner with the attributes of

Chen (2001), Huang (2002), Horzum (2011), and Sandoe (2005) identified and graded

against the proposed study’s needs. As a result, the Structure Component Evaluation

Tool (Sandoe, 2005) emerged as the favored instrument.

Theoretical Foundations and Conceptual Framework

Five-Factor Model of Personality. In a first of its kind review, Allport and

Odbert (1936) noted over 18,000 unique words extracted from the dictionary that are

useful for describing an individual. In order to manage this large list, the scientists

categorized the words into four general groups: personality traits, temporary states,

judgments of personal conduct and reputation, and physical characteristics (John &

Srivastava, 1999). Cattell (1956) addressed Allport and Odbert’s list of over 4,500

personality descriptors and began to organize the characteristics by broad, but unique,

categories, developing 20 primary clusters of personality descriptors, and then eventually

landed upon 16 personality factors, or the 16PF model. Cattell (1956) derived the 16PF

model through factor analysis of the descriptive traits, which described the trait

categories by lexical similarities. Words that tended to mean the same or that exhibited a

similar characteristic were grouped together. Using Cattell’s 16 factors, Tupes and

Christal (1992) tested eight large sample populations across the personality traits, and

then conducted factor analysis of the results. Through this testing, Tupes and Christal

identified five primary personality traits, which became known as the Big Five: surgency,

also known as extroversion, agreeableness, dependability, emotional stability, and

culture. It is also significant that the traits they identified are global descriptions that are

bipolar in nature along a continuous spectrum, suggesting that each trait describes a

characteristic that has two extremes and a continuum of values in between. For example,

surgency included one extreme of extroversion and the other as introversion.

Contemporary Big Five models were born from the work of Tupes and Christal

(1992). A current model is the Five-Factor Model (FFM), which describes that

personality descriptors can be grouped into one of five traits: Openness to Experience,

Conscientiousness, Extroversion, Agreeableness, and Neuroticism (McCrae & Costa,

2003). FFM was developed as a result of examining the covariance of descriptors, such

as those identified by Allport and Cattell, and determining the natural clustering of the

adjectives which describe an individual’s preferences and tendencies (Soto & John,

2012). McCrae and Costa (2003) describe each of the FFM traits in the following

manner. Openness to Experience describes the degree to which an individual is willing

to experience something new, whether it is an idea, a new food, an imaginative thought,

new art, or an activity. Conscientiousness describes the group of facets that explore an

individual’s competence, organization, dutifulness, deliberation, and planning for the

future. Extroversion clusters those facets of personality that describe an individual’s

social interactions, with interpersonal and temperamental traits including warmth,

gregariousness, assertiveness, activity, excitement seeking, and positive emotions.

Agreeableness embodies the characteristics of trust, compliance, and tender-mindedness.

The last trait, Neuroticism, addresses emotional states and expression, with key facets of

anxiety, angry hostility, depression, self-consciousness, impulsiveness, and vulnerability

to stress. FFM is widely accepted to account for natural personality variations between

people (McCrae & Costa, 2003; Thalmayer et al., 2011).

FFM is a lexical approach to provide a common language within the scientific

community and does not attempt to describe how an individual develops a personality.

However, FFM offers three criteria to support its validity (McCrae & Costa, 2003). First,

FFM suggests that an individual’s personality dimensions are summarized by a taxonomy

of five traits, and that any descriptor within the English language, or the many other

languages that have been tested, complies with one of the five categories (Soto & John,

2012). Second, FFM traits are measureable, and that using any of a variety of validated

and reliable instruments, an individual’s personality can be enumerated for comparative

purposes (Thalmayer et al., 2011). Lastly, FFM traits are stable across an individual’s

lifetime (Wortman et al., 2012). Although it is arguable whether personality is a

biological function (McAdams, Gregory, & Eley, 2013), an environmentally caused

attribute (Beijersbergen, Juffer, Bakermans-Kranenburg, & van IJzendoorn, 2012), or a

result of the interaction between genetics and environment (Winham & Biernacka, 2013),

the literature demonstrates that an individual’s personality remains stable across their

lifespan, with exceptions for individuals who experience neurological damage or disease

(Briley & Tucker-Drob, 2014; McCrae & Costa, 2003), and within temporary state

changes based upon situational circumstances (Yeager et al., 2014).

Each of these three criteria was significant to this study. In order to compare

personality traits, which were variables in this study, to the learner outcome variable, all

recognized personality traits had to be accounted for in order to determine whether or not

personality traits shared a relationship with the learning interaction. It was equally

important that each trait was measurable in order to quantitatively assess the relationship

between the trait and the learner outcome variable. Stability of personality traits is

important for individual learners in order to provide consistency of learning environment

preferences, which is a condition that affords the individual the opportunity to maximize

learning success (Hsieh, Lee, & Su, 2013).

Transactional Distance Theory. In the early 20th century, as formalized

education began to take root, educator and philosopher John Dewey opined that humans

are social by nature and derive a sense of self from interactions with others and

environment (Mason, 2013). Dewey went on to describe an individual’s interaction with

others and their surroundings as transaction, an interplay resulting in the individual

experiencing either a sense of connection or distance based upon situational factors. The

concept of transaction continued to develop within education, spawning theories of

experiential learning (Ord & Leather, 2011), and social constructivism (Willey & Burke,

2011), each of which described that learning occurs as a measure of the quality of the

interaction between the individual and the learning environment.

The distance-learning environment presented a new set of circumstances for

consideration within the context of Dewey’s transaction. Whereas Dewey’s

circumstances were considered within the context of individuals being in the presence of

others, distance learning created a new format of presence, and, subsequently, a new form

of distance. Michael Graham Moore addressed this new phenomenon within the Theory

of Transactional Distance. Because the learner and instructor are physically separated

within the distance-learning environment, each must cross a psychological and

communication space in order to create the interplay necessary for learning (Moore,

1993). This psychological and communication space leaves the potential for

misunderstandings and lack of engagement, necessitating special patterns of behavior in

order to bridge the divide. Moore (1993) described this psychological and

communication space as transactional distance.

Transactional distance (TD) is a measure of the relative relationship strength

between the learner and the instructor, and is dependent upon the elements within the

learning situation; namely, the behaviors of the learner and the teacher, and those factors

within their mutual environment (Moore, 1993). While TD is able to measure the

relationship strength in a face-to-face environment, it was developed with geographical

separation in mind (Reyes, 2013). Moore’s (1993) Transactional Distance Theory (TDT)

identifies three interrelated clusters of behaviors and factors that demonstrate influence

within the relationship and govern TD: dialogue, structure, and learner autonomy.

Dialogue. Dialogue describes the broad spectrum of purposeful, positive, and

synergistic interactions between the learner and the instructor (Moore, 1993). Dialogue

connotes the idea of multi-directional communication for the purpose of clarifying,

understanding, and furthering the learning of the student, and does not include the act of

programmed content delivery (Giossos et al., 2009). The learning interaction depends

significantly upon the ability of the learner to communicate with the instructor, and, as a

result, is dependent upon the structure of the curriculum, a relationship addressed in the

discussion of structure (Chen, 2001). Additionally, dialogue is influenced by

environmental factors, such as the number of students to whom a teacher must tend, the

frequency of opportunity for communication, the emotional environment provided by the

instructor, and the psychological disposition of the learner (Moore, 1993). Specifically,

Moore (1993) addressed that dialogue was influenced by the personality of the teacher

and the learner, a concept relevant to this study.

Structure. Structure refers to instructional design by which the curriculum is

delivered to the learner via the prescribed communication medium (Ustati & Hassan,

2013). Concepts, such as the flexibility of the instructional design to adjust to the

learner’s needs and the ability for the technology to accommodate the instructional

design, are included within the structural taxonomy, as are pedagogical considerations of

educational objectives, learning content, assessment activities, and addressing student

motivation (Horzum, 2015). Structure and dialogue demonstrate a consistent inverse

relationship in distance learning in which high structure environments produce low

dialogue opportunities, while low structure designs encourage dialogue (Larkin &

Jamieson-Proctor, 2015). Highly structured environments, such as traditional video

delivery of content, account for each element of content and time with little opportunity

for deviation from the curriculum (Benson & Samarawickrema, 2009). High structure

environments are associated with large TD due to the inability to shift instruction based

upon learner needs (Park, 2011). On the other hand, low structure designs allow for

broad flexibility within the course, including varied frequency and size of content

delivery, altering syllabus direction to expand upon topical concepts, and adjusting

content based upon learner inputs. Due to the capacity for improved understanding and

clarification based upon learner feedback, low structure environments are associated with

small TD (Benson & Samarawickrema, 2009; Park, 2011).

Learner autonomy. Learner autonomy addresses two principle concepts within

the learning environment. The first is the amount of flexibility a learner is provided by

the learning structure to determine learning objectives, create knowledge, and achieve

goals (Moore, 1993). This first concept demonstrates the strong relationship between

structure and learner autonomy in which a highly structured environment imparts low

learner autonomy, whereas a low structure environment allows for learners to choose

syllabus make-up, learning activities, and resources, demonstrating high learner

autonomy. The second concept of learner autonomy includes the psychological view of a

learner’s willingness or ability to be self-directed (Liu, 2015). Learner autonomy

requires that the learner possess the skills and experience to engage in independent study

as well as to be suitably motivated, organized, and open to self-study. Both concepts—a

facilitating structure and a psychologically prepared learner—are essential for high

learner autonomy. High learner autonomy is associated with low structure environments

and low dialogue environments, resulting in high transactional distances (Park, 2011), as

there is less interaction between the learner and the instructor.

Although Dewey utilized the term transaction to describe the interplay between

the learner and the classroom learning environment (Mason, 2013), Moore’s (1989)

Transactional Distance Theory utilized the term interaction to describe the same

phenomenon within the distance learning environment. Moore identified three learning

interaction types that may exist within the distance learning and e-learning environments:

learner-instructor, learner-learner, and learner-content (Anderson, 2003; Moore, 1989).

The learner-instructor interaction describes a relationship between two people in

hierarchical roles in which the instructor provides feedback, dialogue, and motivation,

which is most commonly associated with the traditional teacher-student roles. The

learner-learner interaction describes the exchange of information between peers, which is

typical within social learning environments and online discussion groups. The learner-

content interaction describes the exchange of intellectual information between a learner

and the material, such as a computer application, online materials, or video source.

Chen (2001) introduced a fourth interaction, the learner-interface relationship, to

account for the influence of communication devices and software interfaces that regulate

the learner’s interaction with the instructor, content, and peers. A learner may engage in

multiple interaction types within a single learning environment based upon the dialogue,

structure, and learner autonomy afforded the learner by the instructional design (Moore,

1993). As a result, the phrase learning environment is consistently used throughout the

literature to describe the setting in which the four types of possible learner interactions

transpire (Chen, 2001; Ustati & Hassan, 2013). When a specific relationship type is

salient to the discussion, it is uniquely identified.

Understanding TDT assists in identifying the characteristics of each learning

environment, including the asynchronous video e-learning structure, which subsequently

offers the opportunity to relate these characteristics with learner personality traits.

Defining a learning environment’s factors of dialogue, structure, and learner autonomy

provides a standard by which each learning environment’s characteristics may be

compared, providing greater insight into the relationship between learning environment

characteristics and learner personality traits. The ultimate goal is to develop a

compendium of environmental circumstances that best match with each combination of

learner personality traits in order for instructional designers to develop courses intended

to maximize a learner’s outcomes. This study examined the relationship of personality

traits with transactional distance within the asynchronous video e-learning environment.

TDT provided the opportunity to categorize the dialogue, structure, and learner autonomy

elements of the video environment so that this and future research may compare the

relationship between personality traits and TD with specific levels of dialogue, structure,

and learner autonomy, offering predictive capabilities as future pedagogical and

technological methods emerge that exhibit similar characteristics.

Review of the Literature

This literature review examines individual preferences for interacting within a

learning environment with the purpose of understanding individual characteristics that

influence a learner’s interaction within online environments, and for the purpose of

informing curriculum design in the online environment. The review begins with an

exploration of current learning themes, which includes learner interaction and learning

environment constructs through active learning (Lucas et al., 2013; Thomas & Macias-

Moriarity, 2014) and learning styles (Bhatti & Bart, 2013; Black & Kassaye, 2014). It is

through this initial discussion that the concept of the learner interaction as a salient

variable evolved.

Various learning environments are then explored with a focus on the learner

interaction, yielding evidence that the learner’s satisfaction with the learning environment

varies with factors specific to each learning setting (Islam, 2012; Secreto &

Pamulaklakin, 2015). The learning interaction discussion begins with a thorough

exploration of the online environment through the lens of TDT, including the interaction

types and TD factors that explain learning outcomes (Ali, Ghani, & Latiff, 2015; Hsia et

al., 2014; Papadopoulos & Dagdilelis, 2007). TD is defined as the measure of the online

interaction quality and intensity (Ustati & Hassan, 2013) and satisfaction (Horzum, 2011)

and, consequently, is a variable of interest within the literature. Various online delivery

settings are explored, concluding with the various uses of video technology within the

online environment (Barkhi & Brozovsky, 2003; Falloon, 2011; Ljubojevic et al., 2014).

With the second variable of TD and the characteristics of learning environments defined,

the discussion transitions to an exploration of psychological constructs that may be

related to a learner’s interaction choices.

The discussion develops the relationship of FFM personality traits with other

psychological constructs (Batey, Booth, Furnham, & Lipman, 2011; Caprara et al., 2011;

Hetland et al., 2012; Hertel et al., 2008). The review then explores the confluence of

personality traits with active learning environments, learning styles, and various online

learning environments, demonstrating personality as a variable that correlates with the

learner interaction within each learning environment (Bolliger & Erichsen, 2013).

Asynchronous video e-learning is identified as an environment in which the learner

interaction as a function of personality traits has not been explored. However, two

personality traits, Extroversion and Agreeableness, are shown as being related to

behavior within other video environments (Barkhi & Brozovsky, 2003; Borup et al.,

2013; Maltby et al., 2011; Tsan & Day, 2007), suggesting that this research examine their

effects as variables within the asynchronous video setting.

The chapter continues by clearly stating the gap in the research of the relationship

between personality traits with transactional distance within the asynchronous video e-

learning environment was unknown, and that exploration into this missing evidence was

warranted (Bolliger & Erichsen, 2013). With the research gap identified, the review

transitions to examining methodologies and research designs used to examine

relationships between personality traits and individual behaviors (Blignaut & Ungerer,

2014; Pretz & Folse, 2011; Reyes et al., 2015; Rumrill, 2004). Research instruments are

then discussed in order to identify the appropriate tools for addressing the gap in the

research (Chen, 2001; Costa & McCrae, 1995; Feldt et al., 2014; Horzum, 2011; Huang,

2002; John, 2009; John & Srivastava, 1999; Dwight et al., 1998; Sandoe, 2005). The

chapter concludes by introducing the need to detail the selected methodology and

research design necessary to explore the issue.

Characteristics of learning. Much of the current literature exploring learning

theory centers around constructivist themes with a focus on interaction between the

learner and the learning environment (Ustati & Hassan, 2013). The quality of the

experience is thought to influence learner performance (Mason, 2013). One

constructivist approach is active learning, which describes individuals who engage in

learning activities demonstrating increased performance and skills (Ito & Kawazoe,

2015).

Active learning. Lucas, Testman, Hoyland, Kimble, and Euler (2013) sought to

determine the effectiveness of active learning strategies in a series of courses.

Participants included 70 fourth-year students in a doctoral of pharmacology program who

participated in three pharmacotherapy courses. The first course was a lecture-based

course, and the second and third courses used active learning strategies. A

comprehensive exam was given that included questions specific to knowledge from each

course. The results indicated that performance in the lecture-based course was not as

strong as performance of knowledge based on the active strategy courses. The results

suggest that learners that actively engage with the content demonstrate higher levels of

knowledge performance than those that are only consumers of the content. A limitation

of the test includes the temporal distance between the first class and subsequent classes,

resulting in decayed performance on specific knowledge.

However, not all active learning environments produce superior results. Thomas

and Macias-Moriarity (2014) examined the effectiveness of active learning in a clinical

toxicology course used to satisfy requirements for a doctor of pharmacology degree. The

graduate students (N = 45) participated in the quantitative method, quasi-experimental

design study in which both the instructor and students presented course topics. In

addition to participating in peer-to-peer presentations, learners were required to engage in

classroom activities of developing classroom quizzes, rating the presenters, and asking

questions of presenters. In a comparison of posttest scores, student-presented topic

scores and instructor presented material scores were nearly identical, indicating similar

results regardless of whether or not the learners were actively participating in the learning

activity. Learner-oriented factors, such as motivation and intelligence, amongst others,

may have influenced other behaviors leading to test performance.

Learning styles. Another constructivist approach to improving learning outcomes

and describing learner interaction is matching the individual’s learning style with the

instructional environment. Learning style is based upon Kolb’s four approaches to

learning, which describe the learner’s preferences in assimilating knowledge (Chen et al.,

2014). Although learners may exhibit characteristics of any learning style, they tend to

demonstrate a preference for one of four styles: Diverger, Assimilator, Converger, or

Accomodator. Divergers tend to watch and feel, or sense, the instruction and reflect upon

the information shared. Assimilators watch and think, showing an ability to

conceptualize abstract thoughts. Convergers share thinking and doing traits, formulating

an idea of the new knowledge, and then put it into practice. Accommodators integrate

doing and feeling, preferring hands-on experience to determine a comfort level with the

material.

Attempts to correlate learning styles with performance have met with mixed

results. Bhatti and Bart (2013) used the traditional university classroom to examine

whether learning style was predictive of academic achievement. Participants (N = 193)

completed the Kolb learning styles inventory and granted access to school records to

obtain GPA information. GPA reflected course grades across a broad spectrum of

classes, mitigating student preference and self-efficacy within a particular subject. The

results indicated that learning style was statistically significant in determining GPA, with

Convergers garnering the highest scores, followed in order by Assimilator, Diverger, and

Accommodator learning styles.

Black and Kassaye (2014) explored the influence of course design on student

performance in order to determine if learning styles are related to course design.

Students (N = 563) at a large university were enrolled in business classes with three

different instructional styles. The traditional course used typical classroom pedagogy of

lecture and quiz to present information and assess uptake, a format representing limited

interaction. The experiential design engaged learners in practical experiences related to

occupations covered by course content. Experiences included exercises, writing

assignments, and case study of related topics, representative of high interaction. The

participative design allowed learners a great deal of autonomy in selecting the conduct of

the class, including syllabus design, grading options, learning objectives, and classroom

participation models. Learning styles of the students were measured in accordance with

Kolb’s learning stages: concrete experience (CE) learners, reflective observation (RO)

learners, abstract conceptualization (AC) learners, and active experimentation (AE)

learners. It is noteworthy that although Black and Kassaye elected to describe learners by

the learning stage, Kolb learning styles are typically referred by the processes that occur

between the learning stages, such as the Assimilator, which describes the process of

moving from observation and reflecting to abstract conceptualization. Results showed

learner performance in experiential design courses was better than in traditional design

courses. Additionally, in these courses, learners in the experiential design courses held

more positive perceptions of course conduct than did learners within the traditional

courses. Finally, the participative design resulted in more positive student perceptions of

course conduct and higher learner performance than the experiential design. These

results suggest that the learning environment—traditional, experiential, or participative—

exhibits a significant influence on learner performance and attitudinal outcomes. The

conclusions of the study are two-fold. First, active course designs, such as experiential

and participative course designs, are either equivalent or more effective for student

outcomes than traditional designs. The second conclusion is that learning styles

influence outcomes based upon the learning environment. CE learners, for instance,

favored environments that offered engagement and interactivity, which is expected.

However, traditional designs offer enough interaction such that differences in learner

outcomes based upon learning styles are not significant. It is noteworthy that differences

in learner performance were not statistically significant for any of the learning style

conditions within any learning design.

Moayyeri (2015) examined the influence of undergraduate students learning

preferences on language achievement. Participants (N = 360) were undergraduate

students from different academic disciplines at four Iranian universities. A correlational

design was used to examine the relationship between learning style and language

achievement. The VARK questionnaire was used to determine students’ learning style,

using visual, aural, read/write, and kinesthetic as the modalities of interest. A

standardized language proficiency test was used to evaluate learning performance.

Results showed that learning style differences were significant in determining learning

outcomes. Study conclusions suggest that learning style for Iranian university language

learners influenced learning performance, and, more broadly, that learning styles

influence overall performance within certain environments.

Conclusions from Moayyeri (2015) are supported by Hwang, Sung, Hung, and

Huang (2013), which correlated learning styles with academic performance within the

online learning environment. With the goal of showing the importance of adaptive

learning systems based upon learning styles, the researchers presented 288 Taiwanese

elementary students with a choice of online games based upon natural science content.

The two versions of the online game represented the same content, but were presented

with either an autonomous learner condition or a high level of structure. Students were

tested for learning style preference and given a pretest on the material. End of unit

performance was measured with a unit test. The results showed that students whose

learning style matched the style of game they selected experienced greater improvement

of performance scores compared to students whose learning styles did not match the

game style they selected. These results suggest that a characteristic of learning styles in

combination with learning environment conditions influence learner outcomes.

Richmond and Conrad (2012) investigated the relationship between online student

thinking styles and academic performance. Participants (N = 187) were undergraduate

psychology students from seven different classes across three universities. The

correlational design measured 13 independent variables of learning style using the

Thinking Style Inventory (TSI), of which four were significant in determining GPA. The

results show that learning styles positively predicted GPA based upon style type within

the online psychology class environment. Instructional design applications are drawn

from the results, suggesting that course developers consider student learning styles when

analyzing course requirements and designing the curriculum. A recommendation for

future research included investigating the relationship of learning style and learning style

factors in comparison to other performance indicators besides GPA. Learning style

factors, as this chapter later develops, include learner personality traits.

Not all studies demonstrate a relationship between learning style and academic

performance. Hsieh, Mache, and Knudson (2012) investigated the effect of learning style

preferences on performance on multiple-choice examinations. Participants (N = 90) were

students enrolled in a biomechanics class at a state university, who responded to the

VARK Learning Style Inventory to determine learning style preferences. Multiple-

choice exams were given, each reflecting a specific learning style (e.g., kinesthetic

diagrams or text-based descriptors). The results indicated no significant differences in

test results within differing learning style preferences for text-only and kinesthetic

diagrams. Hsieh et al. suggested that learning style might be more accurately called

learner preference, referring to the format the learner enjoys the most rather than the

approach most suited for knowledge acquisition. As developed in personality trait

theory, learner preference is a construct of personality trait taxonomy. The results of the

learning style literature suggest that learning style as a determinant of performance is

inconclusive, suggesting that learning style influences learner satisfaction, but does not

influence performance (Kim, 2013). As such, it is important to investigate a more

fundamental psychological construct that may correlate with the learning environment to

encourage learning.

Learning environments. Within the exploration of active learning and learning

styles were a number of different learning modalities. Lucas et al. (2013), and Thomas

and Macias-Moriarity (2014) explored active learning within traditional classroom

settings, and Hwang et al. (2013) examined learning styles within an online environment.

Moayyeri (2015) and Hwang et al. showed that learning modality influenced learner

outcomes, suggesting that conditions within each learning environment exhibit

characteristics that uniquely influence the learner and that these characteristics should be

further explored. The following section explores three learning environments: face-to-

face, online, and hybrid, revealing the influences of these settings while developing the

measure of perceived learner interaction within the online environment as a variable.

Face-to-face. Hauser et al. (2012) examined the relationship between

transactional distance, computer self-efficacy, and computer anxiety on performance of

computer related-tasks within the face-to-face environment with some participants

sampled from the online environment. Using a quantitative method, correlational design,

the authors measured anxiety, computer self-efficacy (CSE), and transactional distance.

The sample population (N = 240) was from a junior level management information

systems university class and was biased towards the face-to-face environment with 205

participants, with an additional 35 online learners participating. The authors determined

correlational factors for the anxiety-CSE-performance relationship within each learning

environment. Within the face-to-face environment, significant relationships occurred

between each of the variables, which were TD, anxiety, general CSE, and specific CSE.

Additionally, general CSE and specific CSE were related to performance. Within the

online environment, similar relationships were shown, except that no relationship existed

between anxiety and specific CSE. The results described that the strength of the learner’s

interaction with the learning environment influences psychological constructs, which, in

turn, is related to learning performance. Additionally, the results indicated that

psychological constructs, such as anxiety, changed based upon the learning environment.

Online learning. Constructivist approaches within the e-learning and distance

learning environments are addressed by Transactional Distance Theory (Moore, 1989,

1993; Ustati & Hassan, 2013). Supporting the approach that the quality of the interaction

between learner and the learning environment is determinant of learning outcomes, TDT

states there are three learning characteristics for examination, including the learner, the

instructor, and the interaction between the two (Chen, 2001; Moore, 1989). Additionally,

Moore (1993) suggests within each distant learning environment that there are three

factors that influence the interaction strength: dialogue, structure, and learner autonomy.

TDT also states that these interactions may take on any combination of four forms:

learner-instructor, learner-learner, learner-content (Moore, 1993), and learner-interface

(Chen, 2001). The following section discusses learner interactions within differing

learning environments, while emphasizing the environmental learning factors of

dialogue, structure, and learner autonomy.

Dialogue. Zhou (2014) examined dialogue through the effectiveness of instructor

interaction with learners in a global business project. Students (N = 112) were

international graduate students from a variety of countries and who spoke different

languages. The course promoted discussion and reflection upon real-world business

problems. The research question involved determining whether students’ language and

cultural learning outcomes improved with the aid of a faculty language mentor as

compared to an environment having no mentor and greater autonomy. As measured by

the course survey using a five point Likert-type scale, there was a significant difference

in student language learning effectiveness when learners had the assistance of a language

advisor to facilitate dialogue when compared to an autonomous learner. The research

indicated that the ability to communicate with the instructor and with peers leads to

greater interactive effectiveness and stronger performance, results supported by TDT

(Chen, 2001; Moore, 1989, 1993; Ustati & Hassan, 2013).

Dialogue between peers is another condition conducive to increased interaction

and decreased pedagogical distance (Moore, 1989, 1993). Wang and Morgan (2008)

examined student perceptions of the learning environment when instant messaging

software afforded peer-to-peer communication within an online graduate school

environment. Online learners were responsible for preparing a chapter of the course

content and discussing the themes via instant messaging. A repeated-measures design

was conducted to compare student perception of the study conditions between a non-

instant messaging environment and an instant messaging environment. Results indicated

significant differences between conditions for student cooperation, active learning,

contact with instructor, and prompt feedback, demonstrating that learners feel a closer

communication distance when using messaging technology within peer-to-peer and

learner-to-instructor environments as compared to non-instant messaging environments,

demonstrating a preference for instant messaging-enabled environments.

Ali, Ghani, and Latiff (2015) explored the learner-content relationship through the

study of effectiveness within a personal learning environment (PLE), in which e-learning

content is served to the learner based upon learner preferences. The problem Ali et al.

addressed is the issue of cold start, in which the content delivery system knows nothing

about the learner. Ali et al. described a proposed system in which the learner selects

metatags that describe the learner’s interests when registering for the course. These tags

were then compared to historical learner preferences, which allowed the system to

recommend content based upon others’ experiences. Ali et al. used an online dataset of

movie viewers (N = 71,567) to test their system. Participants interacted with the content,

which measured learner interest and provided more or less of the same style of content

based upon participant feedback. The results showed that as viewers interacted with the

content, the precision of the content served, which is the presentation of relevant content,

increased, while content recall, which is the presentation of irrelevant material,

decreased. Increased learner-content communication shortened the pedagogical distance,

as the content was able to deliver information relevant to the learner.

Secreto and Pamulaklakin (2015) assessed learner satisfaction with an e-learning

interface. Feedback was solicited from undergraduate and graduate students (N = 147),

who were involved in online education at the University of the Philippines Open

University. The user interface served as a gateway to the learner’s online education, both

as the content delivery mechanism and as the administrative portal. The mixed-method

design used an online survey to measure learner satisfaction with the portal’s usefulness,

appearance, efficiency, functionality, ease of use, security, and completeness.

Approximately 90% of total participants reported that the online portal was more cost-

effective, time-efficient, and convenient than using in-person transactions for university

administrative functions. Learner satisfaction levels were high in the areas of response to

inquiries, administrative support areas, availability of contact information, simplicity and

clarity of instructions, reliability of networks, and asynchronous access. Other functional

areas received similar high satisfaction ratings of either satisfied or very satisfied,

including usefulness, functionality, efficiency, appearance, ease of use, and

completeness. Based upon written feedback, the learners valued the system as it

provided communication channels to administration, provided timely academic grade

results, and distributed relevant news. According to the authors, the learner portal

delivered a beneficial learner-interface relationship that narrowed the pedagogical

distance between the learner and the university, and the portal served as a gateway

between the learner and the content, instructor, and peers.

Structure. The learning structure defines the pedagogical and technological

boundaries of the e-learning environment, and, as a result, is interrelated with dialogue

and learner autonomy. Papadopoulos and Dagdilelis (2007) studied the formation of

transactional distance within an elementary geometry class based upon structural

restrictions of computer-assisted instruction. Using qualitative methodology, the

researchers provided 5th and 6th grade students a geometry problem and assigned each

student a software program designed to assist in learning the mathematical concept. The

researchers then observed students’ interactions with the software to assess the perceived

transactional distance. Papadopoulos and Dagilelis noted that five barrier types within

this environment inhibited interaction. Each structural obstacle created a wider distance

in the interaction, contributing to a larger transactional distance. The results also

reinforced the definition of structure in which the learning environment, whether

autonomous or restricted, defines the boundaries the learner must maintain and delineates

the allowable level of interaction.

A psychological boundary of a system’s structure is the perceived quality of the

environment. Islam (2012) investigated the role of perceived system quality in users’

choices to continue using an e-learning system. The results indicated that perceived

system quality and perceived usefulness account for a majority of the variance of

satisfaction, which, in turn, significantly influenced continuance intention. A notable

non-significant result was that perceived system quality was not directly related to

continuance intention. In general terms, the study showed that the learning

environment’s structure influenced learning behaviors and attitudes, including a

willingness to continue interacting with the system.

Learner autonomy. The last of the three individual factors that influence

transactional distance is learner autonomy, which describes the flexibility a learner has in

selecting learning objectives, content, and activities. Benson and Samarawickrema

(2009) investigated the influence of learning supports, which are the concepts that govern

the level of learner autonomy within a learning environment. Using a qualitative method,

case study design, the authors examined six cases with widely varying distance-learning

environments to determine the level of dialogue, structure, and autonomy, with the

ultimate purpose of using this information to inform instructional design. A conclusion

Benson and Samarawickrema reached is that certain circumstances, such as low dialogue

and low structure and high dialogue and high structure dictated the level of transactional

distance regardless of the learner autonomy. Learner autonomy was more influential in

determining transactional distance in mixed environments, such as low dialogue and high

structure, and high dialogue and low structure.

Learner autonomy also refers to the willingness of the learner to be self-directed

within the e-learning environment, concepts highly correlated to self-efficacy (Bullock-

Yowell, Peterson, Wright, Reardon, & Mohn, 2011) and locus of control (Duman & Sen,

2012). Hsia, Chang, and Tseng (2014) examined this construct as they explored the

feasibility of the technology acceptance model (TAM) to explain employee acceptance of

e-learning systems. The results determined that internal locus of control, which is the

perception that events are under the control of the individual, had a positive effect on

perceived usefulness of the e-learning system, and that internal locus of control had a

positive effect on perceived ease of use. Self-efficacy was positively related to perceived

ease of use and intention to use. The results suggest that there is a strong relationship

between some psychological constructs and a learner’s willingness to interact with the

learning environment.

Although a number of studies have been conducted to investigate the individual

factors of transactional distance, other studies examined complete systems, which is the

construction developed by the integration of dialogue, structure, and learner autonomy.

The overall purpose of these studies is to determine the characteristics of the selected

environments and to establish the effectiveness of the chosen interaction level to

encourage learning. A concept that emerges from the study of learning environments is

that psychological factors appear to be related to transactional distance, and that each

learning environment consists of a unique combination of TD factors.

Computer-aided instruction. Murphy and Rodríguez-Manzanares (2008)

researched the effectiveness of high school distance education (DE) as measured by

transactional distance theory using case study methodology. Results indicated successful

academic performance and learner satisfaction in DE requires building rapport and

community in the e-classroom, to which there are many obstacles both within the system

and by way of student and instructor personality. Students reported mixed perceptions as

to whether they were more successful in an asynchronous environment compared to a

synchronous environment, with relationship skills as a proposed explanation for the

difference.

Kizilcec and Schneider (2015) examined the effects of motivation types on

learning behavior outcomes within the online learning environment. The researchers

conducted a quantitative method correlational design with 71,475 participants from 14

Stanford University massive open online courses (MOOC), which are free, non-credit

courses available to the public. Results indicate that motivational intentions were

predictive of student behavior within the online classes. Individuals that expressed

scholastic or professional motivations (e.g., relevant to current studies, professional

advancement, and professional certificates) completed high percentages of the optional

assignments, but participated in few discussion posts. Participants whose motivations

were ego or socially-oriented (e.g., prestigious university, participate with others, and

meet new people) completed few assignments, but responded to at least 50% of

discussion posts. The authors reflected that motivational intentions influenced learner

behavior within the autonomous MOOC environment, with learners selecting the

activities they thought would most benefit their goals.

Video learning. The previously discussed studies examined emerging pedagogies

and technologies of their era. An old technology that continues to be technologically

improved for e-learning is video. This section explores the traditional use of video within

the classroom as a supplementary material (Ljubojevic et al., 2014), video’s evolution as

a two-way communication format (Falloon, 2011), video’s use as a primary instructional

source (Kim & Thayne, 2015; Simonds & Brock, 2014), and then its transition to hybrid

environments (Moffett & Mill, 2014; Velegol et al., 2015). The section concludes by

exploring the literature on the emerging technology of asynchronous video e-learning

(Vural, 2013).

Ljubojevic, Vaskovic, Stankovic, and Vaskovic (2014) explored the efficiency of

use of supplementary video content in multimedia teaching within the face-to-face

classroom. The experimental design used one of seven experimental conditions: class

lecture with no video, class lecture with related educational content video at the

beginning, middle, or end of the class, and class lecture with entertainment

supplementary video positioned at the beginning, middle, or end of the class. The results

indicated that video enhanced learning within the classroom, regardless of the type of

video, but that video related to the content and that was played in the middle of the

instruction resulted in the highest level of learner performance. The authors suggest that

the video medium enhances the learning experience.

Falloon (2011) addressed students’ perceptions of the virtual classroom’s effect

on relationship formation and communication with instructors and peers using qualitative

methods, and investigated which aspects of the classroom most affected students’

engagement in the virtual classroom. The virtual classroom was a synchronous online

communication system that allowed students to see and hear the material, instructors, and

peers within the class in real time. The interpretive case study method found that many

learners built trust and rapport between peers and with the instructor within the

synchronous video environment due to the high quality of the interaction that comes with

real-time video conversation. Communication was effective because students could see

facial expressions and hear tone of voice within conversations. Improved relationship

strength, as identified by trust and rapport, led to shorter transactional distance.

However, some participants expressed reluctance in the virtual environment, citing

concerns over “looking silly” (p. 205) or needing time to reflect prior to responding to

classroom discussion. Falloon identified the lack of understanding about the interaction

effect of personality within a synchronous learning structure upon transactional distance

as a limitation of the study. Falloon suggested student preferences and personality may

be considered an influencing factor of dialogue and learner autonomy.

Kim and Thayne (2015) examined relationship-building strategies for

asynchronous video-based instruction. Using experimental design, the researchers were

interested in whether the learner-instructor relationship could be developed through the

asynchronous video medium. The investigators use a two-group repeated measures

design to compare the treatment conditions and time upon learner attitudes, learner self-

efficacy, and learning performance. The results showed that video instructors that

intentionally exuded warmth and caring, and that used personal, relatable examples

engendered more favorable attitudes from learners than straight-forward, unemotional

instructors. The inclusion of affective traits by the instructor maintained a preferable

attitudinal state in the learners, illustrating the moderating influence of the learning

environment and a factor for instructional design consideration. Learner attitudes

correlate with personality traits, suggesting that personality may influence learner

satisfaction and continuance within the course. It is noteworthy that no significant effects

were seen within the two video conditions for learner self-efficacy, learner-instructor

relationship, module completion, or learning gains.

Individual attitudes and perceptions are also influenced by humanlike

characteristics exhibited by non-human entities. Using a repeated-measures design

ANOVA, Broadbent et al. (2013) examined the differences in perceptions of robot faces

by patients during medical procedures. Medical robots designed to perform basic

functions, such as taking blood pressure, were configured with one of three video screen

faces to look like a human face, silver face, or no face. Patients rated the robot’s

personality, mind, and eeriness in each condition. Robots with the human face on the

screen were rated has being almost humanlike, alive, sociable, and amiable. The results

support theory of mind principles that individuals assign human characteristics, feelings,

and associated attributes to non-human objects when a human characteristic, such as a

face, is displayed. Patient perceptions of the humanlike robot led to greater trust, higher

perceived capabilities of the machine, a sense of agency on behalf of the robot, and a

higher sense of relationship between the patient and the care-giving robot.

Simonds and Brock (2014) explored age-based learning preferences in online

video courses. The mixed-methods design surveyed learners about their learning

preferences within various e-learning environments. The results were statistically

significant for differences in e-learning preferences based upon learner age, with older

learners preferring to watch archived lectures asynchronously and preferences for

watching prerecorded video lectures. A salient learner comment was, “Instructor

comments and videos help one to feel more connected when the face-to-face aspect is not

present with this type of learning” (p. 10). Within interviews, the young learners

expressed a greater interest for learning activities, such as discussion group comments,

interactions, and synchronous interactions, over asynchronous video lecture. The results

of this study inform future design considerations when creating curricula for broad

audiences. The research also informs the present study of the potential for age to be a

confounding variable.

Vural (2013) investigated the effect of activity-based video e-learning on student

achievement. The quasi-experimental design compared learner performance following

learners watching an online instructional video with standard playback controls and no

required interaction, and learners watching online videos in which interactive questions

were embedded into the video, requiring the student to accurately respond to content-

related questions in order to continue viewing. The results showed statistically

significant differences in learner performance with learners who engaged with interactive

learning performing better on the end of course quiz than learners experiencing only the

lecture. The results are in alignment with transactional distance theory (Moore, 1993), in

which environments that support greater learner-learning environment interaction, thus

reducing transactional distance, lead to higher performance.

The examination of the video environment revealed that a variety of individual

learner factors, such as personality, level of control, and age, play a role in the

development of a relationship between the learner and learning environment.

Hybrid environments. Video technology played a role in developing a specific

type of blended learning environment known as the flipped classroom (McCallum,

Schultz, Sellke, & Spartz, 2015). According to Gross, Marinari, Hoffman, DeSimone,

and Burke (2015), the flipped concept emerges from an inversion of traditional classroom

models where content is delivered in the classroom and the learner independently

accomplishes the learning activities (e.g., homework). Within the flipped classroom

environment, content is delivered in an independent, asynchronous manner to the learner,

and then learning activities, such as assessments, projects, and writing, are accomplished

in the classroom environment with the benefit of instructor and peer scaffolding.

Velegol, Zappe, and Mahoney (2015) examined the flipped classroom through

evaluation of students’ interactions, preferences, and performance. Using case study

design, the researchers examined two versions of the flipped classroom. The first version

of flipped classroom used recordings of in-class lectures to create 40 videos of 50-

minutes time each. The second version used professional production techniques to create

11 self-contained modules, each with seven to 18 short video segments, with a maximum

length of 20 minutes time. The results indicated that learner engagement with the content

was strong regardless of flipped classroom version. Learners regularly re-watched videos

when the content was unclear. When attendance in class was optional, students tended to

attend classes to participate in activities, indicating a preference for using the in-class

time for problem solving rather than listening to lectures. Learners also preferred shorter

video lengths—10 minutes or less—even though they were required to watch more

videos. Learning performance as measured by final exam grades across semesters

showed no significant difference between traditional and flipped classroom methods.

When given a choice between taking a traditional class or flipped class in the future, over

three-quarters of students stated they would prefer the flipped class. Students expressed

three reasons for preferring the flipped class: flexibility in learning, the ability to re-watch

lectures, and instructor and peer interaction for homework problem solving. Student

responses highlight the influence of learner autonomy and the presence of dialogue in

determining the level of learner interaction, functions determined by the learning

structure and availability of dialogue. The authors recommended measuring additional

psychological constructs in flipped environments and using quantitative measures to

triangulate their research.

Moffett and Mill (2014) evaluated the use of the flipped classroom approach on

the effectiveness of training. In this experimental design, 197 postgraduate veterinary

students participated in both a traditional classroom course and a flipped classroom

course with video-delivered content teaching separate topics. The results indicated

statistically significant differences with traditional classroom learners showing better

performance than the flipped classroom learners. There were statistically significant

differences between student preferences between the two environments, with learners

favoring the flipped classroom. Despite preferences for a flipped classroom, learner

performance was better in the traditional format, a difference that may be explained by

the disparity between the two course topics.

Psychological constructs in the e-learning environment. Evidence of

psychological constructs is threaded throughout the reviewed literature. Murphy and

Rodríguez-Manzanares (2008) cited relationship skills as a potential factor in

strengthening transactional distance, Falloon (2011) noted the potential relationship

between personality and the willingness to interact within a two-way video environment,

and Velegol et al. (2015) and Moffett and Mill (2014) showed that learner preferences

swayed attitudes towards learning environments. This section reviews the literature

related to principle psychological constructs represented in the learning-based literature.

The review examines the literature associating personality traits with interactions (Kickul

& Kickul, 2006; Kim, 2013; Orvis et al., 2011), which leads to the establishment of

personality traits as primary variables for use in examining learner engagement in various

learning environments. Other psychological constructs, such as attitudes (Broadbent et

al., 2013; Kim & Thayne, 2015), self-efficacy (Caprara et al., 2011; Hsia et al., 2014),

and motivation (Batey et al., 2011) are shown to co-vary with personality traits,

confirming the use of personality traits as a variable.

Personality traits. Personality traits have been shown to predict learner

interaction and behavior within a variety of environments. Hertel, Schroer, Batinic, and

Naumann (2008) examined the role of personality traits Extroversion and Neuroticism on

media preference for communication. Media that is rich has the ability to communicate

in a timely manner and the availability to interpret communication cues surrounding the

message. Formats with low media richness include email and messaging, and high media

richness includes face-to-face and telephone. The results indicated that extroverted

participants preferred rich media compared to introverted participants, and trait

Neuroticism was negatively correlated with rich media, suggesting that individuals with

social anxiety prefer asynchronous communications, such as text or email.

Gosling, Augustine, Vazire, Holtzman, and Gaddis (2011) examined the role of

personality traits in online social network participation. Online social network

participation was measured by activity on Facebook, including number of posts, number

of groups, and number of total friends in network. Results showed significant

correlations between Extroversion and normal social media activities, such as posting

photos, joining groups, and making comments. Trait Openness to Experience was

positively related to the number of friends. Gosling et al. demonstrated that personality

traits directly relate to online interactions within social environments, which may offer

insight into peer-peer learning interactions.

Attitudes, self-efficacy, and motivation. A psychological construct associated

with learner interaction within active learning is learner attitudes. As part of a broader

study examining the socio-technical systems theory, Wu and Hwang (2010) explored

whether learning attitudes positively influence students’ use of e-learning. 1,227 students

from National Taipei University participated in the quasi-experimental design with

results indicating that attitudes exhibit a direct positive relationship with the use of e-

learning. Wu and Hwang concluded that a student’s learning attitude amplifies the

positive effects of a good e-learning system.

Attitudes are associated with personality traits throughout the literature. Hetland,

Saksvik, Albertsen, Berntsen, and Henriksen (2012) explored the relationship between

personality traits and attitudes through the specific attitude of over commitment. The

results indicate that four of the five FFM personality traits are significantly related to the

attitude of over commitment, with positive correlations in Conscientiousness,

Neuroticism, and Openness, and a negative correlation with Agreeableness. It is

noteworthy that each of the FFM traits, except Extroversion, is related to attitude,

indicating that personality traits influence a factor related to interaction in learning.

When examining the relationship between self-efficacy and personality traits,

Caprara, Vecchione, Alessandri, Gerbino, and Barbaranelli (2011) found that FFM

personality traits Openness and Conscientiousness moderated self-efficacy. Caprara et al.

used a sample of 412 Italian high school students within a quantitative, longitudinal

design. Neither Openness nor Conscientiousness was significant in its direct contribution

to academic performance, indicating the role of personality trait may be related to other

functions within the learning environment.

Just as Caprara et al. (2011) and Hetland et al. (2012) examined the relationship

of psychological constructs with personality, Batey, Booth, Furnham, and Lipman (2011)

also investigated the interrelatedness of personality with contextual factors, in this case,

motivation. The results showed significant relationships between personality traits and

facets of motivation, including Extraversion and status, Agreeableness and communion,

and Conscientiousness and accomplishment. Batey et al. suggested that because

personality is a stable characteristic with a strong biological origin (see McAdams et al.,

2013), it is probable that personality is causal in the relationship with motivation. This

logic would also apply in relationships between personality and other psychological

constructs, as well.

Personality and learning. The preceding review of the literature developed the

case for variables worthy of examination; namely, personality traits as a variable,

transactional distance, or interaction strength, as a second variable, and each learning

environment as consisting of a unique combination of TD factors. The following review

examines the literature in which these variables were explored, providing guidance for

the exact applications of such variables and setting precedence for how such studies

should be undertaken. The review begins with a look at personality traits and active

learning environments.

Previously reviewed studies showed the potential for active learning strategies to

produce equivalent results to traditional methods, and the potential for psychological

constructs to influence performance based upon the environment. It has also been shown

that not all learners engage equally with classroom activity. Rodríguez Montequín, Mesa

Fernández, Balsera, and García Nieto (2013) studied how differing combinations of

student personality profiles would explain group interaction and project success. Groups

were assigned an engineering project to complete, and students were asked to rate peers

within the group based upon participation, leadership, and contribution to the overall

project. The study compared personality types of the leaders with project success, but the

authors were unable to draw a correlation between a particular MBTI type and the

group’s success. However, participation within the groups was dependent upon the

personality type of the leader and the personality types of the group members. Some

group members did not participate or did so with low motivation and low creativity,

while other groups experienced high participation rates with activity by individuals

appearing to be a function of the environment and the learner.

Killian and Bastas (2015) found that students engaged with active learning

achieved equal performance outcomes when compared to those engaged in lecture-based

learning. Using a sample of 74 college students from two separate classroom sections

engaged in a sociology class, the researchers applied lecture-based instruction to one

section, the control, while utilizing team-based learning, in which learners were

responsible for teaching concepts, with the second section. Differences in post-course

exams caused the researchers to reject the hypothesis that activity-based learning resulted

in improved results when compared to static-based learning. It is noteworthy that attitude

indices were significantly higher for activities involving greater levels of interaction.

This study was limited based upon using a single active learning strategy. The

researchers recommended continuing to examine the relationship between learner

attitudes and other psychological constructs in relationship to learner performance in

active learning strategy. Killian and Bastas recognized that specific psychological

constructs may correlate with specific learning environments in a positive manner, while

others will have no or negative effect.

A third psychological construct associated with active learning environment

outcomes is motivation. Using the backdrop of the economic principle of the Prisoner’s

Dilemma in which rewards are presented based upon the combination of choices between

two participants, Byun (2014) examined the connection between active learning and

performance as moderated by motivation. Participants were 71 students enrolled in a

university economics course. Following instruction on the Prisoner’s Dilemma model,

students were placed in their own dilemma with their grades at stake. Motivation was

measured as a function of the choice each student made between being cooperative,

which is the safer, but a guaranteed punitive position, or non-cooperative, which is a

riskier, but potentially more rewarding position. Following the activity, the results

indicated that the non-cooperative and more motivated students demonstrated better

performance throughout the course. There was a moderately negative correlation

between cooperation, which is lower motivation, and classroom performance, suggesting

that motivation is moderately correlated with classroom performance and that

cooperation with others is dependent upon the learning circumstances, risks, and rewards.

Personality and learning styles. Following the path of examination of learner

performance as influenced by active learning approaches and personality traits, other

constructivist styles took the same approach. Furnham (2012) examined the relationship

between learning style, intelligence, and personality, and these characteristics’ ability to

predict exam success one year later. Personality traits Conscientiousness and

Agreeableness were each positively related to exam performance. Learning styles were

also related to performance, with deep learning styles—those that seek to achieve full

understanding of the material—negatively correlated with performance, while achieving

styles—those that do the amount of preparation necessary to achieve a high score—

showed positive relationships with performance. The results imply that learners that

attempt a full understanding of the material do not score as well on exams as those more

focused on the extrinsic motivator of the exam grade. Personality traits were related to

exam performance with limited variance due to learning style.

Because of the increasing interest in the potential relationship between personality

and learning style, Threeton, Walter, and Evanoski (2013) investigated the relationship

between personality types and learning styles within the trade and industry sector of

career and technical education. Within active learning strategy and learning style

approaches to performance, numerous factors appear to influence learning performance.

However, the relationships between the specific construct and performance have proved

elusive. When evaluated for common psychological constructs that might explain learner

interaction and performance, one contributory factor that is consistent is personality

traits. The results showed that one personality type, vocational personality type Realistic,

represented 84% of technicians, suggesting that each environment attracts certain

personality types. Learning styles tended to be more equally distributed. Although the

study did not correlate vocational personality type with learning style, it did demonstrate

a self-selecting tendency between the personality types within the automotive repair

industry.

In order to address conflicting results from previous studies correlating learning

style and performance, Kim (2013) explored the effects of Big Five personality traits and

Kolb’s learning styles to identify any relationships with performance. Students (N =

200) from a blended communications university-level course participated in this

correlational design. The results indicated correlations with course grades and traits

Conscientiousness and Extroversion. There were no significant correlations between the

learning styles and course grades, but there were relationships between personality traits

and learning styles. Kim provided data to support the conclusion that correlational

differences between learning style and performance might be reconciled when learning

style is examined as a function of personality traits, suggesting engagement and

performance within a learning environment is more closely related to personality than to

the incumbent learning style.

Personality within online environments. In addition to personality being linked

with overall performance within the active learning environments, personality has been

linked specifically to the interaction between the learner and the learning environment.

Orvis et al. (2011) studied the relationship between personality and learner preference for

control, a quality of learner autonomy, in an e-learning environment featuring interactive

video instruction. The study explored whether trainees were better suited for e-learning

with high learner control compared to low learner control based upon certain personality

characteristics. Results indicated that Openness to Experience and Extroversion

correlated with learner control preferences. The authors recommended similar research

with other e-learning formats.

Kickul and Kickul (2006) investigated the relationships between student

characteristics, such as learning goal orientation and proactive personality, which are

defined by Crant et al. (2011) as the characteristics of one who scans for opportunities

and persists to bring about closure, influenced the quality of learning and satisfaction

within computer-assisted instruction (CAI) learning environments, and learning

outcomes. Graduate and undergraduate students (N = 241) who were enrolled in an

online course participated in the study. The study compared independent variables of

personality types and goal orientation with perceived quality of learning and satisfaction

as dependent variables in order to determine the relationships. The results indicated that

proactive personality characteristics and learning goal orientation were correlated with

perceived quality of learning and overall satisfaction. Student comments, such as the

following, suggest a higher level of interactivity from learners with proactive

personalities:

I particularly like the discussion portion of the classroom or online setting,

as it is a very meaningful part of how I learn. The online forum actually

has allowed me to participate in discussions all week versus one night a

week. (p. 369)

Although proactive personality does not directly correlate with a Big Five trait, it does

suggest an inherent individual tendency for learning.

Al-Dujaily et al. (2013) examined the relationship between personality and

outcomes of learners using computer-based learning systems. The findings showed that

MBTI personality types are related to online interaction choices by learners. Individuals

high in type Extroversion preferred environments offering learners greater control over

the system, while learners low in Extroversion showed greater activity within systems of

high structure and low learner control. Additionally, learners with high Thinking types

were more successful with procedural tasks, and Feeling types were more successful with

declarative knowledge tasks. Additionally, the technology-familiar participants exhibited

self-efficacy with the learning system, which may have masked facets of personality for a

sample population with less technical skill, a consideration when selecting participants

for future research.

Providing additional quantitative investigation into student personality effect in e-

learning, Chang and Chang (2012) investigated the relationship between learning

performance, e-learning, and personality traits within the computer-assisted instruction

environment. The correlational study of 226 Taiwanese participants addressed the

question of whether or not personality traits are related to activity and performance

within an online learning structure. The personality scale used by Chang and Chang

included Extroversion, Neuroticism, and Impulse Control, which were derived from

Singh (1988, as cited in Chang & Chang, 2012). The results showed personality traits

Extroversion, Neuroticism, and Impulse account for some of the variance in learning

interaction and performance, leading to the conclusion that a composite of personality

traits is statistically significant in determining the success of e-learning students. The

personality axis of Impulse Control is not widely used, limiting its comparative value.

However, the overarching results demonstrated the relationship between personality traits

and learner activity and performance conforms to similar studies.

Bauer, Brusso, and Orvis (2012) examined the relationship of personality traits

Openness to Experience, Neuroticism, and Conscientiousness with task difficulty

changes within a military first-person shooter video game-based training environment.

Task difficulty is defined as the degree to which a task represents a personally demanding

environment requiring a large amount of cognitive effort in order to improve the learner’s

knowledge and skills. Participants higher in Openness to Experience performed better in

conditions in which the task difficulty increased or decreased based upon participant

performance, and participants lower in the trait performed better in conditions that did not

experience changes of task difficulty. Results demonstrated that participants higher in

Neuroticism performed better in adaptive difficulty environments compared to static

difficulty conditions. Similar to previous research, personality traits are correlated with

learner behavior within a learning environment.

Bolliger and Erichsen (2013) investigated the differences in perceived student

satisfaction due to personality types in online and blended learning environments.

Student satisfaction was highest amongst learners with MBTI type Extrovert.

Additionally, type Sensor learners preferred online dialogue and independent work

compared to type Intuitive learners. Learner behavior was influenced by personality

type, a factor significant to the present study. Bollinger and Erichsen identified a need

for continued research in this area, specifically the gap in the research of understanding

the relationship between learner personality types and traits, and interaction within

emerging instructional technologies.

Personality and video. Borup, West, and Graham (2013) examined how learner

characteristics engaged with others in an asynchronous video e-learning environment.

Using case study methodology, the researchers examined students’ behaviors within an

asynchronous video e-learning discussion board, which requires learners to record a

video of themselves responding to the discussion board prompt or to other participants’

video posts. The significant findings of the case study analysis showed that an

extroverted learner engaged with the video discussion board in order to earn participation

credit, and was comfortable expressing her thoughts through the medium. She valued

making comments, but did not value the comments of peers. The introverted learner,

who was typically uncomfortable engaging in live classroom discussions, valued the time

available to formulate her thoughts and commit them to video. The introverted learner’s

experience within the asynchronous video environment is in contrast to the learners of

Falloon (2011), who felt they looked silly within the synchronous two-way video

classroom. A difference between the asynchronous and synchronous conditions is the

individual’s ability to process her thoughts prior to committing them to the class. The

cases point toward individual psychological characteristics and motivations as regulating

the level of engagement within the video environment. Additionally, the research points

towards trait Extroversion as having an influence on learner interaction within the video

environment.

Barkhi and Brozovsky (2003) investigated the perception and performance of

individuals with differing MBTI types in traditional face-to-face classrooms and

individuals enrolled in distance classes facilitated by two-way video. The researchers

examined individual preferences of media richness based upon those MBTI types to find

that MBTI type Feeling perceived the rich, two-way video communication to be an

appropriate manner by which to communicate within the course. On the other hand,

MBTI type Intuitive preferred lean communication types, such as email and messaging.

The study informs future studies, including this study, that MBTI type Feeling is known

to influence behavior within the video environment. MBTI type Feeling correlates with

FFM trait Agreeableness (Furnham et al., 2003).

Maltby, McCutcheon, and Lowinger (2011) examined the relationship between

FFM personality traits and celebrity worship, which is a strong psychological absorption

with an on-screen persona in an attempt to establish a sense of identity and fulfillment.

Characteristics of celebrity worship include fantasized conversations with the actor and

increased attentiveness to the on-screen persona’s words and actions. The basic level of

celebrity worship is Entertainment-social, which states that individuals learn about the

on-screen actor to fulfill social needs and provide opportunities for conversation, and is

not considered to be unhealthy behavior. Other levels of celebrity worship are Intense-

personal and Borderline-pathological, which include increasing intensity of personal

feelings and perceived sense of relationship towards the celebrity, and are considered

unhealthy behaviors. The researchers examined correlational tendencies between the

FFM traits and the three levels of celebrity worship, finding that trait Extroversion

exhibited a significant positive correlation with Entertainment-social levels of celebrity

worship. The results suggested that individuals exhibiting a higher level of trait

Extroversion perceived a higher level of relationship with the on-screen persona and

tended to be more attentive to the actor’s words and actions. The viewer’s perceived

dialogue and subsequent attentiveness is postulated as being due to the viewer creating a

cognitive space in which to create a dialogue and a schema in which the celebrity can

exist, resulting in greater attentiveness and less distraction due to cognitive dissonance.

This review examined the literature surrounding the topic of learning interaction,

interaction within the online environment, and psychological constructs that have been

reported to influence the learning interaction. Consistently throughout the literature, the

primary unit of measure was the individual and the behavior that results, which in the

learning environment was the learning interaction and learning outcomes. As a result, the

literature consistently used individual learner characteristics, such as age (Simonds &

Brock, 2014), learning style (Furnham, 2012; Kim, 2013; Threeton, Walter, & Evanoski,

2013), and personality type (Bauer et al., 2012; Orvis et al., 2011), as a variable. The

variables for comparison in the respective studies were learner outcomes, such as

interaction preferences (Huang, 2002) and performance (Barkhi & Brozovsky, 2003;

Bauer et al., 2012; Chang & Chang, 2012). Although the literature thoroughly examined

personality traits as a variable and interaction measurements as a variable for comparison,

the literature was incomplete with regard to the various factors within emerging

modalities, which Bolliger and Erichsen (2013) identified as a gap in the research.

Environments of computer-aided instruction (Kickul & Kickul, 2006), game-based

learning (Bauer et al., 2012), two-way video (Barkhi & Brozovsky, 2003), hybrid (Al-

Dujaily et al., 2013), and face-to-face (Furnham, 2012) have been explored with the

defined variables; however, the related literature is devoid of asynchronous video e-

learning research, a gap this study addressed. Borup et al. (2013) and Barkhi and

Brozovsky (2003) identified a relationship between personality traits and video

environments. In summary, the relationship of personality traits with learner interaction

as measured by TD within the asynchronous video e-learning environment was explored.

Methodology. The nature of a research study’s design is influenced by the

research questions to be answered, the hypotheses that result from the research questions,

and the variables that are measured (Ingham-Broomfield, 2014). Each study’s variables

exhibit unique characteristics that inform the design of the study. In the present study,

the use of personality traits as variables establishes parameters for which research design

consideration was given. Such considerations included the inability to manipulate the

variables and the ability to measure the variables within the non-experimental

environment. It is within these boundaries that the methodology and research design

suitable for the present study was explored.

Quantitative versus qualitative methods for personality research. Two research

methodologies are available for examining gaps in the literature: qualitative and

quantitative. Each method presents strengths and weaknesses for answering certain gaps

within the literature. Qualitative research, for example, offers the ability to identify

psychological characteristics within specific environments. One example is Ma and Zi

(2015), which explored and delineated common characteristics of college students with

perfectionism. The researchers utilized a narrative qualitative research method to

examine the life stories of students who exhibited strong tendencies of perfectionism. Ma

and Zi conducted semi-structured interviews with nine college students. Following the

interviews, the text was examined and coded for themes, which were then compared

across the interviews in an iterative manner until dominant themes emerged. Results

were compared to perfectionism surveys that were administered at the beginning of the

research. The results identified that perfectionists focus upon self-control, status and

success, and love and friendship, with learners that display negative affect personality

traits showing a desire for powerful energy, a sense of control, and status. Although Ma

and Zi provided valuable insight into personality research by identifying perfectionism

themes useful for future research, these types of results were not appropriate for

addressing the question of determining relationship strength between the personality traits

of interest and transactional distance.

A review of the extant literature indicated that the appropriate approach for

addressing measurable relationships is quantitative methodology, the results of which

provide an enumeration of the relationship useful for addressing the primary research

questions. The specific design for investigating relationships involving variables is

correlational design. Correlational design is appropriate for examining relationships

between variables, particularly those of in situ or self-reported medical and psychological

environments. Within correlational design research, variables are compared to determine

the nature and magnitude of the relationship shared between the two (Rumrill, 2004). It

is important to note that correlational designs demonstrate the strength of relationship

between the variables, but do not establish cause. Analysis of regression measures the

strength of the relationship by determining the degree of shared variance, which

expresses how predictive one variable is of another (Meyers et al., 2013). The literature

demonstrated the appropriateness of correlational design for examining the relationship

between personality traits and other criterion variables, such as transactional distance,

and the suitability for analysis of regression for examining the predictive nature of

variables upon outcomes.

Pretz and Folse (2011) examined the relationship between both nursing

experience and intuition in decision-making within the clinical environment. Student and

practicing nurses (N = 175) participated in this correlational design. In addition to

general nursing experience, the study focused on the participants’ use of intuition, which

is exhibited as the responses that are reached with little or no cognitive effort, or

conscious awareness or deliberation. Although it is not defined within FFM, intuitive

behavior is similar to personality trait responses as a natural behavior within a specific

situation or environment, and is measured within MBTI as a bipolar type Intuitive-

Sensate. Intuition was measured using several instruments, including Miller Intuitiveness

Instrument, MBTI, Types of Intuition Scale, and the Smith Intuition Instrument. Factor

analysis was used on individual scales to assess clusters of similar factors, identifying

five primary factors within the Miller instrument and six within the Smith instrument.

Correlational analysis demonstrated that the intuition factors for the Miller and Smith

instruments were positively related to decision-making within the nursing environment,

but not necessarily within the construct of general decision-making. When nursing

experience is included as an independent variable, factor analysis indicated that

experience and intuition were positively related: the greater the experience, the greater

the intuition within the nursing environment. The study’s design utilized factor analysis

when comparing variables with multiple factors, which provided understanding of the

relationship between factors in order to create useful clusters of traits. Valuable

information necessary to answer the research questions was provided by comparisons of

the factor groupings to decision-making, which was a result of correlational analysis.

Reyes et al. (2015) examined the relationship between two dimensions of

perfectionism and depression. The correlational design study examined 173 gifted

Filipino adolescent students (38% males) using a depression inventory and a

perfectionism scale designed for children and adolescents. Perfectionism is defined as

having two facets. The first factor, socially prescribed perfectionism (SPP), is an

introjected phenomenon in which the individual strives to meet a level of excellence due

to the perceived desires or expectations of others, such as parents. The second facet, self-

oriented perfectionism (SOP), is an intrinsic motivation in which the individual sets his or

her own standard of achievement. In either case, the achievement of perfectionism is

unattainable, which creates an environment of perceived failure. Reyes et al. used

correlational analysis to examine the relationship between depression measures and each

of the perfectionism factors, finding that SPP and depression were moderately correlated,

while SOP and depression were not related. The correlational design was effective at

determining the relationship between perfectionism and depression. Reyes et al.

paralleled the requirements of the present study in independently evaluating two non-

manipulated variables against a single, self-reported dependent variable.

Shun et al. (2011) examined the relationship between personality type and quality

of life measures for patients with colorectal cancer. The researchers examined Type D

personality facets, which are associated with personality traits of negative affectivity and

social inhibition, and are measured using the Type D Scale-14 (DS-14). Quality of life

was measured using four different surveys that addressed various aspects of quality of

life, such as fatigue, anxiety, and depression. Patients (N = 124) completed the surveys

at the conclusion of their primary treatment. Shun et al. utilized correlational design to

determine that both facets of Type D personality were significantly related to all of the

quality of life outcomes measured by the four surveys, reaching the conclusion that

negatively oriented personality types—those that are prone to a negative disposition or

those who anticipate a negative outcome—experience a lower quality of life during

treatment. Analysis of regression indicated that certain facets of personality, such as

social inhibition, were predictive of quality of life.

Opateye (2014) examined the relationship between emotional intelligence, test

anxiety, stress, academic success, and attitudes of high school students within the subject

of electrochemistry. Participants in the study were 600 high school students in Lagos,

Nigeria. Opateye utilized correlational design to investigate emotional intelligence’s

relationships with academic success and attitudes towards the subject. A separate

analysis was conducted to examine stress level’s relationships with academic success and

attitudes towards the subject. A third analysis compared test anxiety to academic success

and attitudes towards the subject. The results indicated a significant negative relationship

between stress and academic success, and a significant positive relationship between

emotional intelligence and attitudes towards electrochemistry. Similar to previous

research, correlational design was the appropriate approach for addressing the

relationship between a non-manipulated variable and an outcome.

Blignaut and Ungerer (2014) explored the relationship between Big Five

personality traits and customer service center job performance within the banking

industry. Sampling 89 agents from within a banking group, the researchers utilized a

correlational design to assess the relationships. Personality was measured using the

Occupational Personality Questionnaire 32r (OPQ32r) instrument and job performance

measures were based upon biannual performance assessments. Factor analysis of the

OPQ32r validated the instrument’s use as a FFM measure. Correlational analysis found

that a significant relationship existed between trait Openness and the performance

criterion adhere to and live values, and between trait Agreeableness and performance

criterion emails or calls versus cases ratio. Analysis of regression determined that a

small, but significant amount of the total variation was due to either Agreeableness or

Openness. The results of the analysis addressed the research question of whether or not

job performance measures are influenced by any of the Big Five personality traits, a

question that is similar to the research questions of the present study.

Jong (2013) investigated the possible influence of personality traits, mindfulness,

and spirituality in contributing to the variance in maturity. Using a sample of 537

Chinese adults, the researcher used Pearson correlation analysis and multiple regression

analysis to explore the relationships between the three variable sets. Mindfulness was

measured using the Five Facet Mindfulness Questionnaire, a 39-item instrument of five

subscales: observing, describing, acting with awareness, non-judging of inner experience,

and non-reactivity to inner experience. The Spiritual Transcendence Scale, a 23-item

instrument for measuring prayer fulfillment, universality, and connectedness, was used to

measure spirituality values. The Mini-International Personality Item Pool, a 20-item

measure of Five-Factor Model traits developed for international use, was used to measure

Extroversion, Conscientiousness, Openness, Agreeableness, and Neuroticism. Maturity

was measured using several scales: The Personal Growth Scale measures psychological

well-being using a 14-item self-rating instrument along a six-point Likert-type scale; the

Self-Actualization Index measures psychological actualization using a 15-item instrument

along a four-point scale; and the Self-Report Altruism Scale, which is a 20-item

instrument using a five-point Likert scale to measure the perception of an individual’s

altruistic feelings and behaviors. Correlational analysis described numerous significant

positive correlations between most spiritual, personality, and mindfulness subscales and

maturity measures. Hierarchical regression analysis was used to measure to what degree

domains of personality, mindfulness, and spirituality explained maturity. The results

indicated that personality traits explained approximately 5% of the variance in maturity,

mindfulness explained 8.5% of the variance in maturity, and that spirituality predicted

5.6% of the variance in maturity. The results also indicated that the combined effect of

spirituality and mindfulness explained an additional 14.2% of the variance in maturity

beyond their individual influences.

Saricaoglu and Arslan (2013) investigated the correlation between personal traits,

self-compassion levels, and psychological well-being, and sought to learn if personal

traits and self-compassion levels were predictive of psychological well-being. The

researchers measured traits of 636 Turkish university students using the Psychological

Well-being Scale, an 84-item instrument measuring six sub-factors; the Adjective Based

Personality Scale, a 40-item instrument to measure FFM personality traits; and the Self-

Compassion Scale, which measures levels of self-compassion along a five-point Likert

scale. It was determined that psychological well-being positively correlated with self-

compassion, and that correlations between psychological well-being and personality traits

were significant. Extroversion was positively correlated to psychological well-being

subscales of environmental mastery and self-acceptance, and Openness was significantly

correlated to subscales autonomy, personal growth, and responsibility. Regression

analysis determined that Extroversion, Neuroticism, and self-compassion were predictive

of 34% of the variance within the subscale positive relations with others. Self-

compassion, responsibility, Extroversion, and Neuroticism, were predictive of 49% of the

environmental mastery subscale. Overall, personality traits and self-compassion

explained 44% of the variance of self-acceptance. Correlational analysis and analysis of

regression were effective at addressing the research questions of determining correlation

and predictive capacity of the independent variables upon the dependent variable.

The literature demonstrated the usefulness of correlational design for determining

the measure of a relationship between a non-manipulated personality variable and a

second measurable variable, and that analysis of regression was suitable for examining

the degree to which a variable is predictive of an outcome.

Instrumentation.

Big Five measurement. Five-Factor Model traits are lexical clusters of

descriptive facets that describe a person’s tendencies (McCrae & Costa, 2003).

Behavioral adjectives are compared through factor analysis to identify facets that exhibit

strong covariation, which are then clustered under common trait descriptions. FFM

identifies five personality trait clusters as Extroversion, Agreeableness, Openness,

Conscientiousness, and Neuroticism. An appropriate instrument is one that reliably

identifies an individual’s proclivities within the associated trait description. The seminal

work of Tupes and Christal (1992), which codified the efforts of Allport and Odbert

(1936), and Cattell (1956), performed a series of factorial analyses of behavioral facets to

arrive at the Big Five traits, which were further refined by McCrae and Costa (2003) to

develop FFM. A review of the literature identified instruments that reliably match an

individual’s tendencies with the appropriate trait, and then establishes the validity of the

instrument.

Costa and McCrae (1995) investigated the ability to measure each of the Big Five

domains using descriptive adjectives. The proposed instrument was the NEO PI-R, a

240-item inventory that asks participants to rate the applicability of a single adjective as

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self-descriptive. The authors proposed that by examining participant self-ratings of 30

behavioral facets that the NEO PI-R instrument would produce an accurate assessment of

the individual’s personality traits. Within the instrument, six facets, such as Extroversion

facets of warmth, gregariousness, assertiveness, activity, excitement seeking, and positive

emotions, represent each trait. Costa and McCrae conducted a factor analysis of the data

from the proposed instrument based upon completed surveys of over 1,500 participants,

who were employees of a large company. The results indicated that the selected facets

cohered strongly within each domain without sharing significant variance with other

domains. Additionally, because the data is collected based upon facet scales, a value for

each domain can be calculated in order to enumerate the individual’s trait strength. The

instrument reliably measures an individual’s Big Five personality traits, and produces

results that enable each trait to be used for quantitative measures in comparative studies.

In order to address the need for a shorter instrument useful for measuring Big Five

domains, the Big Five Inventory (BFI) was developed. The BFI is a 44-item inventory

that measures facet strength for the Big Five domains by using short phrases in lieu of

singular adjectives. The short length of the BFI is due to its measurement of only two

personality facets per domain in comparison to the NEO PI-R, which uses six facets per

domain. Feldt, Lee, and Dew (2014) investigated the reliability and validity of the BFI

two-facet structure. Undergraduate college students (N = 295) participated in the

correlational design study. The researchers conducted a factor analysis for each facet and

domain, which confirmed the reliability of each facet for measuring the specified domain.

The results indicate that BFI is a reliable and valid instrument, enabling researchers to

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utilize a shorter examination for personality studies, a benefit to relieve participant

fatigue and attrition.

John and Srivastava (1999) compared the BFI and NEO PI-R, along with a third

test, Trait Descriptive Adjectives (TDA) instrument, for convergence, reliability, and

validity. TDA is a 100-item instrument that is similar to NEO PI-R in its use of singular

adjectives to describe facets. The sample was 462 undergraduate college students at the

University of California, Berkeley, who completed the TDA, NEO PI-R, and BFI. With

some minor differences at the facet level, such the inability of NEO PI-R Openness facets

for values and actions to conform to any BFI trait, the BFI, TDA, and NEO PI-R

instruments exhibited a high level of convergence. John and Srivastava demonstrated

similarly positive results for validity and reliability between the tests. The results

suggested that for personality trait measurement, the three instruments are relatively

interchangeable.

Dwight, Cummings, and Glenar (1998) examined the criterion-related validity of

Saucier’s Mini-Markers instrument and TDA. The Mini-Markers measure is a 40-item

adjective checklist that measures Big Five traits, although it is noteworthy that Saucier

recognized trait Intellect in lieu of trait Openness. Dwight et al. recruited 437

participants from undergraduate personality psychology classes. Participants completed

the Mini-Markers and TDA instruments, which were compared through correlational

analysis. The results indicated a significant relationship between Mini-Markers domains

and TDA traits; although inter-scale correlations were slightly lower for the Mini-

Markers instrument than for the full TDA. Internal consistencies for each instrument

were at acceptable levels, demonstrating reliability between the instruments. Hierarchical

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regression analysis comparing each instrument to external parameters, such as academic

performance, suggested criterion-related validity for the Mini-Markers instrument.

Considerations for selecting the research instrument included the facet types of

interest to the researcher, which favored using the six facet-per-trait NEO PI-R

instrument, and survey length, which favored the 40-item Mini-Markers and 44-item BFI,

each of which takes approximately five minutes to complete. An additional consideration

was that although each instrument described within this review measures Big Five traits,

the Mini-Markers instrument utilized facets related to trait Intellect in lieu of Openness,

which is the FFM trait. As a result, the Mini-Markers instrument was not preferred.

While a valid and reliable test, TDA offered neither the benefits of improved facet study

nor decreased survey time. Because facet level investigation was not applicable to the

current study and out of consideration for participant fatigue, the BFI was the best

candidate instrument for measuring FFM traits.

Transactional distance measurement. Moore’s Theory of Transactional

Distance offers that within the distance-learning environment, a pedagogical, not

geographical, space exists between the learner and the instructor. This gap is defined as

transactional distance, which is the distance of understanding that might contribute to a

communication or psychological gap that exists between two people, leading to potential

misunderstandings. In order to apply TDT in a useful manner, the ability to measure TD

must exist. A variety of instruments have been developed to address the need to measure

TD, each suited to particular environments. The following is a review of the available

instruments and a determination of the appropriate measure.

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Chen (2001) developed a 23-item instrument to describe and measure the

potential learning environments that a distant learner may encounter. The purpose for

developing the instrument was to measure the level of transactional distance along four

axes and to validate that each of the four measured interactions was a facet of TD. The

instrument included five items addressing learner-instructor relations, five items

addressing learner-learner interaction, six items referencing learner-content interaction,

and seven items related to learner-interface interactions. The instrument was completed

by 82 online undergraduate and graduate students enrolled in a web-based course at

National Chung Cheng University, Taiwan. A four-factor factor analysis was completed

indicating the four types of relationships investigated by the instrument represented a

majority of the variance for transactional distance, with over half of the variance

represented by learner-learner interactions. Chen demonstrated that TD consisted of co-

varying interactions between the learner and four loci of instruction: the instructor, other

learners, the content, and the interface. High Cronbach’s alpha scores along each axis

indicate internal consistency and test reliability. Chen’s instrument is a good, all-axis

measure of TD; however, a significant shortcoming of the instrument is that it is only

available in Chinese.

Huang (2002) sought to develop an instrument to measure student perceptions

within online courses. The Student Perceptions of Online Courses instrument consisted

of 27 items measuring attitudes towards learner interaction, course structure, learner

autonomy, and learner interaction with the interface. The inventory makes statements

related to sub-factors, such as, “I understand the course content,” and are rated on a

Likert-type scale from 1 to 7 with 1 representing strongly disagree and 7 representing

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strongly agree. Online learners (N = 31) enrolled in Master’s degree courses at Seattle

Pacific University completed the instrument. Stepwise regression results indicated

significant positive relationships between interface and course structure, interaction, and

learner autonomy. Student satisfaction was measured, indicating moderately high levels

of satisfaction within the online courses. Individual student satisfaction ratings are

available for each sub-scale; however, the scale does not calculate an overall measure of

TD. Instructional designers will find the instrument is well suited for evaluating the

construction of a course to determine the relationships between the interface, course

structure, interactions, and learner autonomy.

Horzum (2011) proposed to develop a valid and reliable TD scale. The

instrument consisted of 38 items addressing five sub-factors of dialogue, structure

flexibility, content organization, control, and learner autonomy. Participants were 197

blended learning students from Sakarya University, Turkey. Confirmatory factor analysis

indicated the indices demonstrated an acceptable fit and Cronbach alphas for each

dimension were high, suggesting internal consistency and reliability. The scale was

useful for measuring TD within the blended environment, offering the capability of

measuring student perceptions of elements known to influence transactional distance.

Sandoe (2005) proposed to develop an instrument suited for measuring the

structure component of an online course. The Structure Component Evaluation Tool

(SCET) consisted of 47 items examining eight categories of structure as related to TDT.

Three subject matter experts evaluated 20 online courses according to the instrument

guidelines. Correlational analysis was used to determine the relationship between SCET

and other structural component measures derived from Chen (2001), Huang (2002), and

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Bischoff, Bisconer, Kooker, and Woods (1996), which indicated strong reliability of the

instrument. SCET is designed and developed for comparative research related to TDT,

and issues individual ratings for each sub-factor while providing an overall score for TD.

With the objective of measuring transactional distance within the asynchronous

video e-learning environment, the SCET was the most appropriate tool available. SCET

provided the benefits of providing a single value representative of TD for comparison

against personality trait measures. Additionally, the nature of the SCET is to measure

structure with moderate emphasis on learner autonomy and minor emphasis on dialogue,

characteristics that are well suited to the asynchronous video environment, which features

little learner autonomy and minimal two-way interactions. The instruments developed by

Chen (2001), Horzum (2011), and Huang (2002) would each be effective in measuring

TD within the study. Each instrument measures TD across a broad spectrum of factors,

including interaction types, dialogue, content, and structure, with SCET having a stronger

presence in measuring structure. A strong second instrument choice was Huang’s

Student Perceptions of Online Courses due to the shorter length of the inventory and the

clarity of the item descriptions.

Summary

Educational theorists and instructional designers have long sought to determine

the most effective manner by which a learner can acquire knowledge, a motivation that

has produced numerous approaches to how the human mind works and interacts with its

environment. A current approach is the constructivist approach to learning, which

suggests the interaction between learner and the instructor moderate learning

performance (Mason, 2013). Exploration of constructivist approaches of active learning

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and learning style theory produced mixed results. There is an abundance of evidence that

learning involving salient activities improves learning in classroom environments (Lucas

et al., 2013; Menekse, Stump, Krause, & Chi, 2013; Whyte & Alexander, 2014;

Witkowski & Cornell, 2015). However, there is also significant research to indicate that

active learning is no more effective in performance outcomes than passive learning

(Downs & Wilson, 2015; Killian & Bastas, 2015; Thomas & Macias-Moriarity, 2014).

Learning styles follow the same pattern. Matching learning styles with pedagogy has

been shown to correlate with improved learning performance in classroom environments

(Bhatti & Bart, 2013; Black & Kassaye, 2014; Moayyeri, 2015) and in online

environments (Hwang et al., 2013; Richmond & Conrad, 2012). On the other hand, valid

studies refuted that matching learning style with learning environment improves learning

performance (Furnham, 2012; Hsieh et al., 2012). The evidence from the research

indicates that psychological constructs, such as self-efficacy, motivation, and learner

attitudes, influence various aspects of learning engagement necessary for active learning

(Byun, 2014; Rodríguez Montequín et al., 2013; Wu & Hwang, 2010), and that these

psychological constructs are related to personality traits (Batey et al., 2011; Caprara et

al., 2011; Hetland et al., 2012).

In order to understand the individual characteristics that facilitate greater and

more effective interaction with learning, academic inquiry of learner performance shifted

from active learning and learning styles-learning environment links to a process whereby

the learner’s personality traits influence the learner’s interaction. The results from

classroom investigations indicated that personality has significant influence upon

learning interaction and performance (Kim, 2013; Furnham, 2012; Threeton et al., 2013),

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and that further investigation into the relationship between personality and learner

interaction is warranted.

Most investigations regarding active learning and learning style theory involved

traditional classroom environments (Bhatti & Bart, 2013; Downs & Wilson, 2015; Lucas

et al., 2013; Thomas & Macias-Moriarity, 2014). Within the online environment,

investigations into influencers of learner interaction reach similar conclusions. Factors of

dialogue, learning structure, and learner autonomy were related to online interaction,

which was measured by transactional distance (Moore, 1993), and were independently

investigated, finding that each factor independently contributed to learner success

(Benson & Samarawickrema, 2009; Hsia et al., 2014; Kamaluddin et al., 2014;

Papadopoulos & Dagdilelis, 2007; Zhou, 2014). Scholarly investigations validated the

influence of psychological constructs upon transactional distance across all identified

interaction types of learner-instructor, learner-content, learner-learner, and learner-

interface (Ali et al., 2015; Islam, 2012; Secreto & Pamulaklakin, 2015; Wang & Morgan,

2008).

Investigation continued by examining the interaction of psychological

characteristics within complete e-learning environments, each composed of differing

levels of dialogue, structure, and learner autonomy. Initial qualitative results identified

personality traits as potentially having influence on transactional distance (Falloon, 2011;

Murphy & Rodríguez-Manzanares, 2008), and recommended further investigation into

the phenomenon. Follow-on quantitative investigations revealed definitive relationships

between psychological constructs and perceived pedagogical distance within the e-

learning environment (Hauser et al., 2012; Kizilcec & Schneider, 2015), research that

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recommended that similar investigations be conducted using personality traits as

variables. Research indicated that personality traits were shown to predict the extent of

interaction within online environments, such as social media and interpersonal

communication (Gosling et al., 2011; Hertel et al., 2008), and this research was extended

into the e-learning environment, which demonstrated that personality traits were linked to

the level of learner-learning environment interaction, learner satisfaction, and academic

performance (Al-Dujaily et al., 2013; Bauer et al., 2012; Bolliger & Erichsen, 2013;

Chang & Chang, 2012; Orvis et al., 2011). While each study found that personality traits

were linked to learner-learning environment interaction, there was not a clear pattern

between personality traits and learning environment. As a result, the studies’ authors

recommended the continued investigation into current and emerging environments in

order to provide greater clarity of the personality trait-transactional distance relationship.

One such environment was the video-based learning environment. Although

learner-learning environment interaction had been investigated for video use in a variety

of formats, including the face-to-face classroom (Ljubojevic et al., 2014), two-way

videoconferencing classrooms (Chen & Willits, 1998), and blended environments, such

as flipped classrooms (Moffett & Mill, 2014; Velegol et al., 2015), the emerging e-

learning technology of asynchronous video-based e-learning has only recently begun to

receive attention. Research investigated asynchronous video-based e-learning, including

design methods to improve learner-learning environment interaction (Kim & Thayne,

2015), personality traits within asynchronous video discussion groups (Borup et al.,

2013), practical skills instruction (Buch, Treschow, Svendsen, & Worm, 2014), learner

preferences based upon age (Simonds & Brock, 2014), and the effect of activity-based e-

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learning on student achievement (Vural, 2013). Additionally, it was demonstrated that

personality traits were influential within the video environment (Barkhi & Brozovsky,

2003; Borup et al., 2013; Tsan & Day, 2007), which suggested further investigation of

personality traits’ influence within other video environments. Bolliger and Erichsen

(2013) identified a gap in the research necessitating the investigation of the relationship

between personality traits and learner interaction within emerging learning technology

environments. The present research sought to address this gap by investigating the

question of whether personality traits were related to transactional distance within the

asynchronous video-based e-learning environment and what was the strength of that

relationship.

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Chapter 3: Methodology

Introduction

The purpose of this quantitative correlational design investigation was to examine

the correlation between personality traits and perceived learner interaction satisfaction as

measured by transactional distance in an asynchronous video e-learning environment, and

to determine which personality traits were predictive of TD within the same environment.

Previous research revealed a variety of relationships between personality traits and

learner interaction dependent upon the unique learning environment. Kickul and Kickul

(2006) found that proactive personality traits influenced the quality of learning within

computer-assisted instruction (CAI) learning environments. Hauser et al. (2012)

demonstrated that computer self-efficacy and anxiety correlated with performance in a

hybrid online and in-seat management information systems classes. Orvis et al. (2011)

correlated trait Extroversion and trait Openness to Experience with learner autonomy as

measured by training performance in an undergraduate management course.

The current research identified the gaps within the literature in order to resolve

the question through this study. The research explored personality traits, as measured by

the Big Five Inventory (John, 2009), which is a validated and reliable instrument for

measuring dimensions of personality, and their relationship with transactional distance, as

measured by the Structure Component Evaluation Tool (Sandoe, 2005), a validated and

reliable measure of pedagogical distance. Although this type of research has been

accomplished in the past, this research was unique in addressing the potential relationship

between personality traits and learner interaction within the emerging technology of

asynchronous video e-learning. The following chapter reviews the problem of the study,

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the research questions and hypotheses, the research methodology, and the research

design. Additional information is discussed, including the population sample, the data

collection and analysis procedures, and ethical considerations for conducting the

research. The chapter concludes by describing the limitations of the proposed research.

Statement of the Problem

It was unknown whether FFM personality traits were related to a learner’s

transactional distance within an asynchronous video-based e-learning environment, and

what was the strength of that relationship. The literature demonstrated that personality

traits express a relationship with communication and psychological distance within

asynchronous computer-assisted instruction environments (Kickul & Kickul, 2006), high-

and low-autonomy conditions of CAI (Orvis et al., 2011), hybrid CAI and in-seat

environments (Al-Dujaily et al., 2013; Hauser et al., 2012), and gaming-based learning

environments (Bauer et al., 2012). Bolliger and Erichsen (2013) identified a gap in the

research in which the relationship between personality types, and learner interaction and

satisfaction must be examined within differing and new learning environments. Because

individuals with differing personality traits demonstrate preferences for diverse learning

environments, and matching learners with engaging learning environments maximizes the

individual’s learning outcomes (Kim, 2013), it is important for instructional designers to

design courses that align personality-based interaction tendencies with the appropriate

frameworks to maximize learner outcomes (Benson & Samarawickrema, 2009).

Research Questions and Hypotheses

Scholarly literature discussing the relationship between personality traits and

video viewing or video learning preferences was limited. Within video conferencing

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environments, MBTI type Feeling (Barkhi & Brozovsky, 2003), which most closely

correlates to FFM trait Agreeableness (Furnham et al., 2003), was related to individual

communication satisfaction. Trait Extroversion was related to trust and smaller

psychological distances in two-way video counseling (Tsan & Day, 2007), and was

related to student participation patterns in asynchronous video communications (Borup et

al., 2013). Additionally, trait Extroversion is related to perceived relationship strength

between viewers and on-screen actors (Maltby et al., 2011). However, because

asynchronous video-based e-learning is an emerging environment, it was investigated

through the lens of each of the Five-Factor Model personality traits. Each of the

personality traits represented a personality variable and was measureable for each

participant using the Big Five Inventory, a validated and reliable instrument for

measuring FFM personality traits (John, 2009). Personality trait data was compared to

the participant’s perception of TD, which was the learning outcome variable.

Transactional distance was measured using the Structure Component Evaluation Tool

(Sandoe, 2005). Using personality traits as measured by the Big Five Inventory (John,

2009), and comparing each trait to transactional distance as measured by the Structure

Component Evaluation Tool (Sandoe, 2005), the following research questions and

hypotheses guided this research study:

RQ1: Is there a significant correlation between Five-Factor Model personality

traits and transactional distance within the asynchronous video-based e-learning

environment?

H1A-O: Trait Openness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

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H10-O: Trait Openness does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-C: Trait Conscientiousness correlates significantly with transactional distance

in the asynchronous video-based e-learning environment.

H10-C: Trait Conscientiousness does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

H1A-E: Trait Extroversion correlates significantly with transactional distance in

the asynchronous video-based e-learning environment.

H10-E: Trait Extroversion does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

H1A-A: Trait Agreeableness correlates significantly with transactional distance in

the asynchronous video-based e-learning environment.

H10-A: Trait Agreeableness does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

H1A-N: Trait Neuroticism correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-N: Trait Neuroticism does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning environment?

H2A-O: Trait Openness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

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H20-O: Trait Openness is not significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H2A-C: Trait Conscientiousness is significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

H20-C: Trait Conscientiousness is not significantly predictive of transactional

distance in the asynchronous video-based e-learning environment.

H2A-E: Trait Extroversion is significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H20-E: Trait Extroversion is not significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

H2A-A: Trait Agreeableness is significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H20-A: Trait Agreeableness is not significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

H2A-N: Trait Neuroticism is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-N: Trait Neuroticism is not significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

Research Methodology

The selection of research methodology for personality research generally follows

one of three paths (McAdams & Pals, 2007). The first is the examination of individual

differences, which addresses the questions of what makes individuals different from one

another and what is the structure of human individuality. This line of reasoning emerges

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from personality as a construct of dispositional traits that describe individuality as a

stable, internally manifested set of characteristics (Wortman et al., 2012). These

characteristics are globally defined, but are uniquely displayed within each individual.

Within this construct, individual tendencies are compared to clusters of global traits, traits

that have been identified and clustered through quantitative methodology using factorial

analysis (Tupes & Christal, 1992). Subsequently, the comparison of individual

tendencies to these groups is also accomplished using quantitative methods in order to

compare the lexical strength of the behavioral descriptors. Eysenck described such

comparisons as occurring along a general, bipolar, and linear continuum upon which an

individual’s manifestation may be placed (McAdams & Pals, 2007). The comparison of

these trait strengths to other phenomena is also the domain of quantitative methods.

The second path of personality research is the examination of motivation,

addressing the questions of why do individuals do what they do and what do individuals

want (McAdams & Pals, 2007). Motivation stems from an individual’s unique

psychological orientation towards behavior within a situation, based upon time, rewards,

and social role (Deci & Ryan, 2008). Whether approaching motivational theory as

identifying motivation as a general, unitary construct, or as a distinction between

autonomous and controlled motivations, each is measured by the reporting of behavioral

strengths, motives, or choices, which are quantifiable (Egli, Bland, Melton, & Czech,

2011). These reported motivational scores are then compared to individual preferences

and tendencies within disparate settings, thus proffering quantitative methodology as the

necessary approach to research.

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The third path of personality study explores holism, the understanding of the

whole person and the meaning of life (McAdams & Pals, 2007). Constructs developed

through the study of holism include the life stories of individuals, who integrate the past,

present, and future to provide a sense of purpose and meaning to their existence. In

comparison to personality traits, which are global in nature and capable of scales to

describe each individual, and motivations, which are describable via encompassing scalar

reporting and measurement, life stories are unique to each individual. As such, life

stories cannot be measured via scalar values or predefined categories. Instead, holism is

investigated using qualitative methodology that examines recurrent themes, agency, and

environment.

The factor that determined the methodology path was the theory of interest

(McAdams & Pals, 2007). The theory describes the construct of interest, which, in turn,

designates the examination approach and the variables, if applicable. The theoretical

argument describes the path by which the researcher must examine the phenomenon of

interest and is the lens by which the problem is examined. As a result, one must identify

the determinant theory.

The purpose of this study was to explore the relationship between personality

traits and the learner’s interaction within the asynchronous video e-learning environment,

and to examine the strength of that relationship. The research questions for the study

sought to determine the relationship strength between an individual’s personality traits

and that learner’s perceived interaction strength with the content. The applicable theory

upon which this study was founded is personality trait theory using the Five-Factor

Model (McCrae & Costa, 2003). With this goal in mind, the first path described by

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McAdams and Pals (2007), in which the construct is the dispositional trait, was

appropriate.

In alignment with the first methodological path, FFM utilizes the individual as the

unit of measure by which the trait is measured along a general, bipolar linear continuum,

and is a variable for comparison. A variable is a factor that is changed, manipulated,

observed in situ, self-reported, or controlled in order to determine its effect upon a

condition or its relationship to a second variable (Gravetter & Wallnau, 2013). The

study’s interest was to describe the relationship between the FFM trait and a second

variable, perceived interaction strength, which was measured by TD within the online

environment. The comparative information—that is, the values of each variable—was

collected as quantifiable data. Quantitative research is the methodology for examining

objective theories through the relationships of the variables (Ingham-Broomfield, 2014).

The research question required that variables be examined through statistical

analysis in order to determine significant relationships and to use the best available

evidence to make decisions (Waghorn, Dias, Gladman, & Harris, 2015). In order to do

so, the researcher had to take objective measures within the context of valid theory and

assess the data for statistically significant relationships in order to determine correlation.

The nature of this study was in alignment with positivism, the premise that inquiry occurs

through verified and replicated findings achieved by direct observation of entities or

processes, which is a guiding philosophy of quantitative methodology (Goduka, 2012).

In addition to the study’s theoretical foundation supporting the choice of

quantitative methods, the research question, variables, and desired outcome of the present

study also informed that quantitative methodology was appropriate. The research

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question sought the confirmation or denial of a significant relationship between two

empirically measurable phenomena. The first variable of the study, personality trait, was

measurable, and due to the variation of trait strengths between individuals, was a variable

that changes for each unit of measure, which satisfies the requirement for a variable. The

second variable of the study, transactional distance, was measureable and has

demonstrated change in relation to psychological constructs (e.g., Bolliger & Erichsen,

2013; Hauser et al., 2012; Kizilcec & Schneider, 2015). It was the purpose of this study

to determine whether there was a relationship between the variables of personality trait

and transactional distance within the asynchronous video learning environment, and to

explain each trait’s level of influence was upon transactional distance.

In the past, the choice of quantitative methodologies over qualitative

methodologies was considered superior due to the ability to enumerate the outcomes

(Frost, 2011). However, this argument no longer applies as the use of qualitative

methods has demonstrated significant value within the scientific community. With this in

mind, it was important to establish the benefits of the use of one methodology over the

other when choosing the research approach. In the case of the present study, the use of

quantitative methodology, which was selected due to the use of personality trait theory

and its accompanying characteristics and variables, provided benefits over qualitative

methods. Quantitative methods allowed for the development of hypotheses to be tested,

which guided the selection of variables and research design (Liu, Zhang, Feng, Wong, &

Ng, 2015). On the other hand, qualitative methods looked at the situation globally and

attempted to describe the phenomenon in context, which does not support the desired

study outcome. Quantitative methodology offered exploration that was repeatable,

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reliable, and can be independently verified, which allows subsequent researchers the

opportunity to duplicate the work in whole or in part (Arghode, 2012). Such an approach

was critical for developing an overall view to the relationship between personality traits

and interaction within the abundance of learning environment types, which was essential

to the development of future theory for instructional design.

Qualitative methodology offers the benefit of answering why the phenomenon

occurred, as well as offering alternative options to explore that may not have been

considered within the quantitative research design development (Frost, 2011). An

example is Falloon (2011), which descriptively investigated the interaction of learners

within the two-way video classroom environment. It was through this qualitative

examination that the phenomenon of personality traits as an area of interest evolved,

spurring future research into the area.

Additionally, qualitative methods in personality research serve the purpose of

studying the whole person and developing theories specific to the individual (McAdams

& Pals, 2007). Although qualitative methods are powerful tools for social science, they

were not appropriate for addressing the research gap as addressed in the problem

statement. Each method serves a purpose; in the case of this study, the characteristics of

measuring relationship strength and creating a repeatable design through quantitative

methodology was more fitting.

Research Design

Several characteristics of personality traits influenced design selection: Individual

trait dispositions were unknown prior to testing, the measurement of personality traits

was along a continuous scale (John, 2009), and personality traits cannot be manipulated

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(McCrae & Costa, 2003). Additionally, the purpose of the design was to determine the

strength of relationship between personality and learning interaction variables. The unit

of analysis was the individual person, who provided data regarding their personality traits

using the Big Five Inventory, which measured the personality trait variables (John, 2009),

and perceived interaction ratings using the Structure Component Evaluation Tool, which

measured the variable of transactional distance (Sandoe, 2005).

The design most suited for the selected environment was correlational design

(Jamison & Schuttler, 2015; Rumrill, 2004), which is a frequently used design type in

personality research (Richardson, Abraham, & Bond, 2012). Data collected from a

correlational study must meet the criteria of being continuous in nature, which was true

of FFM traits (John et al., 2008) and TD measurements from the Structure Component

Evaluation Tool (Sandoe, 2005).

The correlational design offered the benefit of identifying associative

relationships between variables and allows the researcher to measure relationship strength

(Rumrill, 2004). Specifically, the design facilitated correlation analysis to determine

whether either personality variable exhibits a significant relationship with TD. By its

nature, correlational design emphasized individual differences and the variability between

variables of interest (Revelle, 2007). The variables included each of the FFM personality

traits, which were independently compared to the variable of transactional distance.

Personality variables were selected based upon their previous relationships upon learner

attitudes and performance within the video environment, including two-way video

distance education (Barkhi & Brozovsky, 2003; Tsan & Day, 2007) and asynchronous

video discussion boards (Borup et al., 2013). Additionally, trait Extroversion was related

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to perceived relationship development and relationship strength between viewers and on-

screen personas (Maltby et al., 2011).

A correlational design was appropriate compared to other designs for the purposes

of this study. A second approach for consideration was experimental design; however,

experimental designs required the manipulation of variables in order to influence the

dependent variable and measure an outcome (Rumrill, 2004). Personality traits do not

conform to the requirements of a variable for experimental design as personality traits

cannot be manipulated; personality traits are fixed and generally stable over the

individual’s lifespan (McCrae & Costa, 2003), and were, therefore, not suited for an

experimental design.

Each FFM trait was measured using the Big Five Inventory, which calculated a

score based upon learner responses. Scores for each trait range were normalized from 0

to 100, with 50 representing the midpoint (John, 2009). Scores higher than the midpoint

represented the high dimension of the traits, such as extroversion, and scores lower than

the midpoint represented the lower dimension of the trait, such as introversion. The

further a score was from the midpoint, the stronger the manifestation of the bipolar trait.

The learning interaction variable was transactional distance, which represented the

perceived relationship strength between the learner and the learning environment. TD

was measured using the Structure Component Evaluation Tool (SCET), which is an

instrument designed to evaluate TD within e-learning environments, such as

asynchronous video settings (Sandoe, 2005). SCET scores ranged from 0, which

represented no perceived learner-learning environment interaction, to 24, which

represented a strong learner-learning environment interaction.

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Pearson correlational analysis was used to examine for a linear relationship

between each personality trait and transactional distance, as it was suited for bivariate

correlation of continuous variables. Similar studies successfully used Pearson correlation

for establishing relationships between personality traits and learner outcomes, such as

attitudes (Kamaluddin et al., 2014) and academic achievement (Caprara et al., 2011). In

the event that one of the variables was determined to be non-continuous, analysis was to

be Spearman correlation analysis, a method suitable for continuous and ordinal data sets,

and an analysis better suited to address outlier data sets (Gravetter & Wallnau, 2013).

Analysis of regression was used to examine the strength of the relationship

between the personality traits and transactional distance. Analysis of regression answers

the question: To what extent does the first variable explain or predict the second variable

by measuring the level of covariance between the variables (Meyers et al., 2013). Similar

studies utilized analysis of regression to explain the influence of personality traits, such

as learning outcomes (Kim, 2013), maturity (Jong, 2013), and psychological well-being

(Saricaoglu & Arslan, 2013). Data for analysis of regression assumes the data is linear,

normally distributed, homoscedastic, the variables are not auto-correlated, and the data is

not collinear (Meyers et al., 2013).

The design included inherent risks. Had the distribution of personality traits not

been normal, producing a restricted range of data, the validity may have been questioned

due to potential covariance between the personality trait variables (Levy & Ellis, 2011).

These risks were mitigated through an appropriate sample size calculated to match the

design, including number of variables, effect size, and statistical analysis method, by

measuring the interaction effect between the two personality variables (Gravetter &

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Wallnau, 2013), and by selecting a diverse sample population (Al-Dujaily et al., 2013). It

is also important to note that correlational designs do not attempt to identify causal

relationships; however, covariation is a necessary condition for causality (Rumrill, 2004).

Population and Sample Selection

The present investigation was conducted in the online environment that was made

of consumers seeking self-improvement through Internet resources. This market includes

e-learning content designed for personal improvement, skills development, and individual

enjoyment, and does not include formal education, such as online universities or trade

schools, and does not include corporate distance learning. Examples of content providers

within this market are Lynda.com, which provides online content of over 3,800 video

courses on topics ranging from personal computer skills, photography, and business

skills; Khan Academy, which provides instruction of math and sciences through hundreds

of video courses; and E-learning Center, which offers hundreds of computer

programming and information technology courses.

The online course selected for the current study was a course independently

developed by the researcher for delivery to couples in a relationship. The course was

used in a premarital education class using a flipped classroom design for an organization

with over 10,000 members in the Phoenix metropolitan area. However, the course was

designed as a standalone product and was employed as such during the study. The course

employed independent learning via asynchronous video instruction with no interaction

between the learner and an instructor or other learners. Each participant experienced the

same three-module course, with each module lasting approximately five minutes,

followed by a learning activity, which was a non-graded quiz. The researcher was the

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developer and primary instructor for premarital classes within the organization. For the

purposes of this study, delivery of the course was exclusively to the general public via the

Internet and was not affiliated with or dependent upon any organization for providing

study participants. As a result, specific site authorization for conducting this study was

unnecessary. However, Institutional Review Board approval was accomplished prior to

proceeding with data collection.

The target population was individuals involved in a relationship who are seeking

to enhance communication skills within the relationship. Participants were respondents

to direct mail and Internet advertising for individuals 18-years of age or older interested

in taking a free online course on the topic of communication skills for relationships for

this convenience sample. Advertising targeted groups involved in a relationship in order

to attract participants interested in communication within relationships. Direct mail

mailing lists targeted suburban single-family home communities in the Phoenix

metropolitan area, where 73% of single-family homes are purchased by either married or

unmarried couples (Snowden, 2015). Internet advertising utilized keywords marriage,

relationship, marriage courses, free online communication courses, and marriage courses

online, within major search engines (Google, n.d.).

Although the opportunity to receive the free course was available to all

respondents, the sample was selected from those who meet a minimum criteria of 18-

years of age and, if it was determined to be a significant covariant with transactional

distance, from those that indicate a minimum level of computer experience of at least 6

hours each week online (Hauser et al., 2012; Siraj et al., 2015). Regulating data by

computer experience reduces confounding variables that are known to influence learner

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enjoyment, satisfaction, and transactional distance (Hsia et al., 2014) and is positively

related to learner performance in self-directed online courses (Simmering et al., 2009).

This study used convenience sampling, which was appropriate for this study because the

sample population was a subset of the general population filtered based upon interest in

communication skills development and was sufficiently similar to the target population

with respect to personality and learner outcomes (Landers, & Behrend, 2015).

Additionally, convenience sampling was a derivative of Internet-based studies, as

anonymous respondents were self-selected based upon the researcher’s advertising and

referral sources (Butler et al., 2005) and based upon individual participant characteristics

(Maloni, Przeworski, & Damato, 2013). A convenience sample in a specific region may

yield different levels of a specific personality trait (e.g., higher levels of Extroversion)

from other regions (McCrae & Sutin, 2009); however, a predominant characteristic of

personality traits is each trait yields consistent and predictable behavior (John &

Srivastava, 1999). Although a sample of balanced age and gender is preferred, these

criteria were not exclusionary, as personality traits are the focus of the study.

Using a bivariate normal model approach for correlation, the G*Power 3.1

software program calculated that a minimum of 84 data sets were necessary for this study

to achieve a power of .80 and a maximum error probability of .05 based upon an

anticipated moderate correlation (r = .3) and a two-tailed test based upon a general

population of greater than 10,000 (N < 10,000) (Orvis et al., 2011; Peng et al., 2012).

Advertising for participants was ongoing and continued for as long as necessary to collect

the required minimum sample size of completed data sets. This approach addressed the

need to ensure a qualified sample population, as well as to address anticipated attrition.

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Institutional Review Board approval was necessary prior to any research

activities, including advertising for participants or collecting data. In order to protect the

privacy of each individual, all data was securely stored offline. Additionally, this study

did not solicit nor collect personally identifying information or metadata; data was

identified by a sequential numbering system in which the first participant was 001, the

second was 002, and so forth.

Instrumentation

Personality trait data was measured using the Big Five Inventory (BFI), a 44-

question instrument that takes approximately five minutes to accomplish (John, 2009).

The instrument measured the strength of the five traits of FFM: Openness to experience,

Conscientiousness, Extroversion, Agreeableness, and Neuroticism. Extraversion includes

five positive descriptors (e.g., talkative) and three negative adjectives (e.g., reserved).

The Agreeableness scale includes five positive adjectives (e.g., helpful) and four negative

adjectives (e.g., aloof). The Conscientiousness measure includes five positive adjectives

(e.g., thorough) and four negative adjectives (e.g., disorganized). Neuroticism is

composed of five positive adjectives (e.g., tense) and four negative adjectives (e.g.,

calm). The Openness to Experience scale includes eight positive adjectives (e.g.,

curious) and two negative adjectives (e.g., prefers routine work). BFI asked respondents

to rate how well itemized phrases (e.g., Is full of energy) described the individuals on a 5-

point scale ranging from 1 (disagree strongly) to 5 (agree strongly). Scores were

calculated for each trait by finding the mean for each scale, with negative phrase values

scored in reverse (e.g., 1 became a 5, 2 became a 4). Mean values above a 3 were

considered positive for that trait, such as extraverted, while mean values below a 3 were

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considered negative for that trait, such as introverted (John & Srivastava, 1999). BFI

data were then normalized as a scalar value from 0 to 100, indicating the tendencies of

the individual’s personality. The scale was bipolar with values less than 50 designating a

preference for the low end of the scale (e.g., introversion) and values greater than 50

denoting a disposition for the higher end of the scale (e.g., extroversion).

BFI demonstrated validity when compared to other Five-Factor Model

instruments. Corrected pairwise convergent validities between BFI and the 100-item

Trait Descriptive Adjectives (TDA) test resulted in a mean correlation of .95. Similar

results emerged from corrected pairwise convergent correlations between BFI and the

240-item NEO-PI-R instrument with a validity coefficient of .92 (John & Srivastava,

1999). The BFI also demonstrated validity when investigated for fit with the two-facet

model (Feldt et al., 2014), and was a validated and reliable indicator of personality across

North American cultures, including amongst African American (Worrell & Cross, 2004)

and Hispanic (Benet-Martinez & John, 1998) populations. Reliability was high for BFI

with an instrument mean alpha of .83. This compared to alphas for the longer scales of

TDA (M = .89) and NEO-PI-R (M = .79) (John & Srivastava, 1999). The instrument was

freely available for non-commercial use (John, 2009).

The second instrument was the Structure Component Evaluation Tool (SCET),

which measured learner affinity with content, delivery, and course interaction along eight

dimensions of transactional distance factors of dialogue, learner autonomy, and structure

using a four-point scale from 0 (not evident) to 3 (fully evident) (Horzum, 2011; Sandoe,

2005). SCET scores range from 0 to 24, with 24 meaning complete affinity between

learner and learning environment, while a 0 is the opposite. An example of a SCET

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inventory item is: Students can proceed at their own pace. Other instruments were

evaluated for use, including those developed in Bischoff et al. (1996), Chen (2001),

Horzum (2011), and Huang (2002); however, SCET was most applicable due to the

highly structured nature of asynchronous video e-learning, the lack of social interaction,

and the limited learner autonomy granted within the selected learning environment. The

Delphi method was used to validate the instrument and statistical analysis and inter-rater

reliability tests demonstrated the internal and external reliability of the tool (Sandoe,

2005). The researcher obtained written permission from the instrument’s author to use

the instrument for this research (see Appendix C). Participant demographic information,

including participant age, gender, and online activity level, was collected as a self-report

survey upon beginning the course.

Validity

The present study examined the extent of the relationship between FFM

personality traits and transactional distance. The variables of personality trait are

measured using any one of several validated instruments. This study used the BFI. The

BFI demonstrated external validity in comparison to two other well-established

instruments, Trait Descriptive Adjectives (TDA) and NEO-PI-R (John & Srivastava,

1999). Pairwise convergent validities were conducted between BFI and the two

instruments, TDA and NEO-PI-R, for each trait and for the complete instruments. BFI-

TDA corrected pairwise convergent validities were Extraversion (.99), Agreeableness

(.93), Conscientiousness (.94), Neuroticism (.90), and Openness (.89), with an overall

mean validity score of .95. Similar comparisons were made between BFI and NEO-PI-R

with the following results: Extroversion (.83), Agreeableness (.97), Conscientiousness

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(.96), Neuroticism (.90), and Openness (.85), with a mean validity coefficient of .92.

Because the variable was measured with the validated BFI instrument, internal and

external validity was sound.

The Structure Component Evaluation Tool (SCET) was evaluated for validity

across several facets (Sandoe, 2005). Subject matter experts in the area of online learning

verified face validity, the concept of whether the instrument does what it suggests.

Content validity is the degree to which the instrument measures the intended

characteristic or concept. Based upon the judgments of subject matter experts, the facets

of measurement areas were correctly grouped into major themes and the SCET items

accurately reflected those themes with no overlap between themes. Convergent validity

demonstrates that related items are significantly intercorrelated, for which a coefficient of

.88 was obtained.

Reliability

Reliability is a measure of an experiment, test, or procedure to yield the same

result following repeated trials (Ingham-Broomfield, 2014). Big Five Inventory was

tested for reliability across all five traits and a mean reliability alpha calculated for the

instrument (John & Srivastava, 1999). BFI alphas for Extroversion (.88),

Conscientiousness (.82), and Neuroticism (.84) indicated high levels of reliability, and

Agreeableness (.79) and Openness (.81) slightly less so. The mean alpha value was .83,

indicating a strong reliability for the instrument. These values for alpha compare to

reliability factors for TDA: Extroversion (.92), Agreeableness (.90), Conscientiousness

(.90), Neuroticism (.85), and Openness (.88), with a mean alpha of .89. Alphas for NEO-

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PI-R were as follows: Extroversion (.78), Agreeableness (.83), Conscientiousness (.85),

Neuroticism (.85), and Openness (.81), with a mean of .79.

Reliability of the SCET was estimated by determining the alpha coefficient for

each category item based upon the consistency of raters’ scores across 20 online courses

(Sandoe, 2005). Alphas for the major categories are as follows: Overall content

organization (.996), syllabus content organization (.980), course schedule content

organization (.949), overall delivery organization (.978), consistency of delivery

organization (.986), flexibility of delivery organization (.800), student-to-student course

interaction (.932), and student-to-instructor course interaction (1.000). Values of these

magnitudes indicate high reliability of the instrument.

Data Collection and Management

An e-learning course module on communication skills for relationships was

introduced to accommodate the aims of this study. The course was hosted on a study-

specific website using Adobe Captivate learning delivery software. Captivate allowed for

the development and delivery of learning content using multiple media types, including

video and text. Additionally, features of Captivate include assessment quizzes and

surveys, which were requirements of this study. The following describes the data

collection and management process, which is summarized in Figure 1.

The sample population, recruited from respondents to advertising for a free course

on communication skills for relationships, was directed to the study website, where the

respondent received an introductory video with instructions for accomplishing the study.

The video welcomed the participants to the study and introduced the overall purpose of

their participation. The video stated that the following was a study examining the e-

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learning environment and that respondent participation was greatly appreciated. The

video continued by describing that participants were required to electronically agree to

the Informed Consent form (see Appendix B) in order to continue to participate in the

study. The video proceeded to describe the need for each participant to complete a short

pre-course demographic questionnaire, and then to accomplish the course segment on

communication skills for relationships. Once the course segment was complete,

participants were asked to complete a post-course survey. The introductory video

concluded by making the participant aware that the completion time required the surveys

and course was approximately 25 to 35 minutes.

The actor within the administrative videos, including the welcome and

instructions, was different than the course presenter. Participants were asked to continue

to the next screen, upon which a video presented the Informed Consent form (see

Appendix B), and then the Informed Consent form was presented for electronic signature.

Upon signing and continuing, participants viewed video instructions for completing the

pre-course questionnaire. The pre-course questionnaire solicited demographic

information, such as age, gender, relationship status, Internet connection type, and

Internet and computer usage habits. The questionnaire continued with the 44-question

Big Five Inventory, which took approximately five minutes to complete (John, 2009).

Upon completing the BFI, participants began the course segment on

communication skills for relationships. The course included three modules with the

topics of the purpose of communication, choices in communication, and ambiguity in

communication. Within each video, the instructor addressed the camera directly using a

blend of information, casual conversation, and personal anecdotes, which are known to

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assist in developing rapport between the learner and instructor within the online video

environment (Kim & Thayne, 2015). Each module was organized with an introductory

description of the module objectives, a five- to seven-minute video describing a facet of

interpersonal communication and practical skills to improve dialogue within the

relationship, and then the module concluded with two multiple-choice questions based

upon the learning objectives. Multiple-choice questions were used to provide a level of

interactivity between the learner and the content. Learners that answered questions

inaccurately were prompted with an on-screen text cue of the appropriate response and

were prompted to reattempt the answer. Correct answers resulted in a short description

expounding the answer and the learner was prompted to continue with the course. Each

participant experienced the same three-module course and the course provided no

opportunities for learner interaction with the instructor or other learners. The

transactional distance factors that describe this asynchronous video course are high

structure due to the rigidity of the course flow (Park, 2011), low learner autonomy as

learners had little freedom to explore information outside of the course, which is a

function of the high structure (Benson & Samarawickrema, 2009), and low dialogue with

learners having had no opportunity to ask questions or clarify concepts with an instructor

or peers (Moore, 1989; Park, 2011)

Following completion of the course segment, participants were presented with a

video providing instructions for completing the Structure Component Evaluation Test,

and then the SCET was presented. After participants completed the instrument, a final

video was presented with appreciation for participating, an explanation of the study

purpose to measure the effect of personality on perceived pedagogical distance, and to

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provide participants with the opportunity to receive a follow-up call or email from the

researcher to address any concerns.

Figure 1. Workflow describing learner path and data collection. The path describes the

learner’s entrance into the site and completing pre-course surveys’, completing the

asynchronous video course, and collecting post-course information, and summarizing the

research for the learner.

The organization of the process was intentional. The research design began by

soliciting information about the participant, both in the form of demographic information

and the BFI instrument. Information was collected prior to the course in order to avoid

any inadvertent priming of the personality traits due to the content of the course (Popov

& Hristova, 2015). Additionally, by identifying the survey purpose early in the study

process, the participant was more likely to answer all the research questions, which

results in higher response rates in surveys requesting personal information (Fang, Wen, &

Learner enters website

Welcome video with instructions

Informed consent instructions (video) and signature

Instructions for pre-course

questionnaire (video)

Demographic survey

Big Five Inventory

Course segment 1 (video)

Course segment 2 (video)

Course Segment 3 (video)

Video instructions and Structure Component

Evaluation Test

Video conclusion, and opportunity

to receive follow- up

Dataset downloaded to

researcher's local server

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Pavur, 2012). The learning content was then presented as preparation for the final

survey, which asked questions about the structure of the content and the participant’s

interaction within the content.

Although no personally identifying information or metadata was solicited or

maintained, the data collected was secured offline so as to minimize any risks of public

exposure of the information or of any harm coming to participants. In accordance with

Grand Canyon University guidelines, data will be kept for at least five years. Data is

stored on a local computer with a backup copy maintained in a fireproof safe. When the

decision is made to destroy the data, the file will be erased from the computer and the

backup copy physically destroyed.

In addition to data-oriented participant rights protection, other ethical

considerations were integrated into the process to minimize participant risks. The

proposed study utilized a video course on the topic of relationships, which held the

potential to unknowingly trigger unwanted thoughts or memories within a participant

(Curci, Lanciano, Soleti, & Rimé, 2013). Preserving participant dignity was a valid

concern of any study involving relationships (Ost, 2013); therefore, participants were

instructed prior to the course that their participation in the study was strictly voluntary

and that the participant may discontinue the course at any time with no further

consequences or negative attribution. Instrument length was kept at the minimum

length—less than 100 questions—to satisfy the study requirements in order to mitigate

participant fatigue (Hoerger, 2010). Lastly, video instruction provided that there were no

socially acceptable responses and, as a result, participants should feel free to select the

answer that was most applicable to their experience, avoiding any challenges to a

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participant’s integrity by implying an expectation of a preferred response (Peter &

Valkenburg, 2011).

Data Analysis Procedures

Data analysis investigated the relationship between the personality trait variables

and the learner outcome variable, and the extent to which FFM personality traits explain

the variance in TD. The present study variables included the BFI measures for

personality trait (John, 2009). The personality traits were independently evaluated

against the learner outcome variable, which was the measure of TD as provided by SCET

(Sandoe, 2005) in order to address the research questions and the accompanying

hypotheses. SCET values are inversely related to TD, such that high SCET values

suggest small TD and low SCET values suggest high TD.

RQ1: Is there a significant correlation between Five-Factor Model personality traits

and transactional distance within the asynchronous video-based e-learning

environment?

H1A-O: Trait Openness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-O: Trait Openness does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

H1A-C: Trait Conscientiousness correlates significantly with transactional distance in

the asynchronous video-based e-learning environment.

H10-C: Trait Conscientiousness does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

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H1A-E: Trait Extroversion correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-E: Trait Extroversion does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-A: Trait Agreeableness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-A: Trait Agreeableness does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-N: Trait Neuroticism correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-N: Trait Neuroticism does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning

environment?

H2A-O: Trait Openness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-O: Trait Openness is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-C: Trait Conscientiousness is significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H20-C: Trait Conscientiousness is not significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

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H2A-E: Trait Extroversion is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-E: Trait Extroversion is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-A: Trait Agreeableness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-A: Trait Agreeableness is not significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H2A-N: Trait Neuroticism is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-N: Trait Neuroticism is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

The analysis began with data validation. Data screening ensured that reported

scores were within acceptable boundaries and evaluated the distribution of missing

variables (Meyers et al., 2013). Missing data was acceptable provided that information

for the personality traits of interest and transactional distance was intact. In cases of

random missing data, defined as fewer than three elements per participant, mean

substitution imputation methods were used; otherwise, pairwise deletion was employed.

Continuous variables were evaluated using data plots, including box and whiskers plots

and scatter plots to evaluate outliers. Descriptive statistics, including demographic

information, were calculated using SPSS, which determined the mean, standard

deviation, skewness, and kurtosis of each variable. A minimum of 84 complete data sets

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was necessary to complete the test for a normal bivariate correlation with a power of .80

and significance of .05.

The test of the first research question was accomplished using Pearson correlation

analysis, which checked for significant relationships (p < .05) between variables

(Gravetter & Wallnau, 2013). Personality trait Openness measures from BFI results were

compared to TD results as measured by SCET. The process was repeated for correlation

analysis between each of the remaining traits and TD. The strength of the Pearson

correlation between each trait and TD determined the outcome for each set of hypotheses

based upon a significance of p < .05. In order to accomplish a Pearson correlation

analysis, four assumptions must be met: the variables must be continuous, there must be

no outliers, the data must be linear, and the data must be normally distributed (Meyers et

al., 2013). These assumptions were examined via visual inspections of the variable

scatter plots, normal Q-Q plots, histograms, and box plots, and by examination of the

distribution’s skewness and kurtosis.

Additional findings beyond the research questions were addressed through other

methods of comparison. Analysis of variation (ANOVA) was conducted between age

groups, online usage, and gender, and TD to explore any significant relationships. These

variables were evaluated by placing data into bins. Age was placed in bins of 10-year

increments (e.g., less than 20 years, 20-29 years, 30 – 39 years, etc.). Online usage was

categorized into groups of time based upon Internet and computer usage each week: less

than one hour, one to three hours, three to six hours, six to nine hours, and nine hours and

greater. It should be noted that learners that use the Internet for six hours or greater have

been shown to perform better in online tasks (Siraj et al., 2015). Gender was compared

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to transactional distance using an independent t-test to determine the significance of the

relationship differences, if any.

The hypotheses for the second research question were tested using analysis of

regression. Personality trait measures as determined by BFI results were compared to

transactional distance measures as described by SCET results. Using hierarchical

regression, each trait was independently compared to determine the extent of the variance

of TD as explained by the personality trait. Significant results (p < .05) rejected the null

hypothesis, and non-significant results failed to reject the null hypothesis. Multiple

regression was used to determine the extent to which personality traits predicted SCET

values. In order to determine the extent to which individual traits predicted SCET

variables, hierarchical regression was used. Hierarchical regression examines each

individual trait as a covariate with the SCET variable to measure the extent to which the

trait variable explains the measure of TD (Myers et al., 2013). Traits are added in steps

or blocks to examine the set of trait variables as covariates of the SCET variable, which

allows the researcher to determine the change in predictability due to the added variables.

The result is that each statistically significant model identifies the extent to which the

block of variables explains the outcome variable.

In order to conduct analyses of regression, the data must conform to the following

assumptions: data must be continuous and linear, the data must not be auto-correlated, the

data must be a normal distribution, it must not be collinear, and the data must be

homoscedastic (Meyers et al., 2013). The assumptions were examined through visual

inspection of scatter plots, normal Q-Q plots, histograms, and box plots, determinations

of skewness and kurtosis, by examining the variance inflation factor, through the use of

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the Durbin-Watson test for each variable, and through the calculation and comparison of

the Levene statistic for values across two groups.

Ethical Considerations

The ethical risks of this study were minimal and were intentionally addressed in

the development of the study procedures. The research for this study was conducted in

the online environment and data was secured to ensure privacy. The online research

environment of random applicants also mitigated the risks of participant interaction that

might influence study results. The researcher submitted the appropriate form for

processing human subjects to the Institutional Review Board (IRB) before collecting data

(see Appendix A). Data was collected online and stored offline, restricting access to only

the researchers and ensuring participant privacy. Demographic information and

instrument data was collected and maintained for the length of the study; however, names

or metadata were not collected in order to maintain privacy. The data will be maintained

for a minimum of five years in accordance with Grand Canyon University research

standards, keeping in mind that the data may be stored for several years longer to

accommodate future study of the interaction effect of all FFM traits with TD. It is

noteworthy that there was no personally identifiable information attached to any data, so

there was no risk of ethical compromise that could cause individual harm. In order to

comply with privacy standards, all participants had the opportunity of opting out of the

personality trait and TD assessments with no further follow-up.

Although materials, such as the video course or survey questions, may have

inadvertently triggered unpleasant thoughts or memories (Curci et al., 2013), the potential

for such episodes were minimized through the use of standardized instruments. Although

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it is unlikely, the video course subject material may have initiated unpleasant thoughts or

memories in some participants. As a result, participants were instructed during the pre-

course video instructions that they could discontinue participation at any time and that

participants may contact the researcher for follow-up, if desired. Voluntary

discontinuation of Internet-based studies typically occurs following reading the consent

form, thereby avoiding significant ethical issues affiliated with fatigue (Hoerger, 2010).

Additionally, the total number of instrument questions was less than 100, suggesting that

participant fatigue should not have been an issue. Although any research project presents

ethical challenges, the present study identified common and anticipated areas of ethical

difficulty and took the appropriate steps to mitigate those risks. As a result, the risks of

participating in this investigation were minimal.

Limitations and Delimitations

A limitation of the study was the research design. Correlational design offered

the ability to determine whether or not a relationship existed between the two

variables. However, because this was a non-experimental design, causation was

unable to be determined (Rumrill, 2004).

The length of the online course was a delimitation of the study. In order to

reduce the attrition rate of participants due to fatigue, the researcher elected to utilize a

short video course as the learning environment. The ability for relationship

development between the material and the participant within such a short span of time

was difficult to measure. Consideration for future research is to include multiple

course segments, such as part of a regular curriculum, in order to measure the

development of a relationship between the learner and the learning environment.

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The use of the SCET (Sandoe, 2005) was a delimitation of the study. Certain

aspects of the instrument, such as the measurement of peer and instructor interaction,

did not apply to the present study. As a result, it was anticipated that the SCET value

data will be right skewed due to lower perceived interaction. The skewed nature of

the results, however, were in alignment with the central limit theorem, which suggests

that the data may be treated as normal despite the skewed nature of the results

(Fitrianto & Hanafi, 2014).

Summary

The purpose of this research was to examine whether Five-Factor Model

personality traits had a relationship with a learner’s perceived interaction satisfaction

within an asynchronous video-based e-learning environment. The question stemmed

from the gap in research that covered constructivist-learning approaches in which

learning occurs as a result of interaction between a learner and the learning environment.

The research investigated many aspects of this relationship, including various

psychological characteristics of the learner, the types of interactions the learner may have

with the learning environment, and the environments in which the learner operates. The

literature distilled the need to investigate personality traits and their influence on learner

interactions, from which the research questions emerge. As a self-regulatory construct,

personality traits emerged as a variable within the study. Because the interaction was the

concept of interest, the measurement of the interaction, which was transactional distance,

becomes the second variable for comparison. With the variables defined and the broad

research questions identified, the specific parameters of the research fell into place, as did

the hypotheses.

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RQ1: Is there a significant correlation between Five-Factor Model personality traits

and transactional distance within the asynchronous video-based e-learning

environment?

H1A-O: Trait Openness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-O: Trait Openness does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

H1A-C: Trait Conscientiousness correlates significantly with transactional distance in

the asynchronous video-based e-learning environment.

H10-C: Trait Conscientiousness does not correlate significantly with transactional

distance in the asynchronous video-based e-learning environment.

H1A-E: Trait Extroversion correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-E: Trait Extroversion does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-A: Trait Agreeableness correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-A: Trait Agreeableness does not correlate significantly with transactional distance

in the asynchronous video-based e-learning environment.

H1A-N: Trait Neuroticism correlates significantly with transactional distance in the

asynchronous video-based e-learning environment.

H10-N: Trait Neuroticism does not correlate significantly with transactional distance in

the asynchronous video-based e-learning environment.

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RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning

environment?

H2A-O: Trait Openness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-O: Trait Openness is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-C: Trait Conscientiousness is significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H20-C: Trait Conscientiousness is not significantly predictive of transactional distance

in the asynchronous video-based e-learning environment.

H2A-E: Trait Extroversion is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-E: Trait Extroversion is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H2A-A: Trait Agreeableness is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-A: Trait Agreeableness is not significantly predictive of transactional distance in

the asynchronous video-based e-learning environment.

H2A-N: Trait Neuroticism is significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

H20-N: Trait Neuroticism is not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment.

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Because the purpose of the research was to measure the strength of the

relationship between the personality traits and TD, and because the variables and their

influence upon the learner outcome variable were observable, quantitative methodology

was the most appropriate approach. Of the design tools available for quantitative

research, the correlational design was the most appropriate for this research due to the

characteristics of the variables and the format required of the answer in order to address

the research question. Correlational design afforded the benefits of identifying

associative relationships and measuring the strength of those interactions (Rumrill, 2004).

The results of the Pearson correlation determined whether the respective traits are

significantly related to the interaction and the analysis of regression determined to what

extent.

In order to accomplish this goal, a sample population of at least 84 participants

was necessary. Participants self-identified through responses to online search engine

advertising and direct mail campaigns in order to achieve the desired count. The traits of

the individuals were measured using validated and reliable instruments, with the Big Five

Inventory measuring the strength of FFM personality dimensions and the Structured

Component Evaluation Tool assessing the strength of the learner’s perceived relationship.

A study-specific website presented the learners with instructions, the Informed Consent

form (see Appendix B), demographic and BFI surveys pre-course, the asynchronous

video-based e-learning course segment, and a post-course survey, which was the SCET.

Once complete, the participants were thanked, presented with an overview of the research

in which they participated, and offered the opportunity for phone call or email follow-up.

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Because of the anonymity of the online environment and that there was no

personally identifiable information collected, ethical issues were anticipated to be

minimal. Additional ethical considerations included the length of the testing, which was

mitigated by minimizing participant time spent within the instrument, which was around

30 minutes, and because voluntary discontinuation tended to assist in avoiding participant

fatigue issues in online surveys (Hoerger, 2010). Several limitations of the study were

identified, which were the characteristics of the online course, including short training

period and the use of SCET as the measurement of TD. Consideration should be given to

including a longer-term video course in future studies.

The following chapter identifies the results of the study and elaborates on the

outcomes. Specifically, Chapter 4 examines the research and demonstrates the

correlational relationships between personality traits and transactional distance, and uses

analysis of regression to determine the ability of personality traits to predict transactional

distance. Additional factors of this study, including gender, device type, and Internet

usage, are also examined to determine whether these variables influence learner

interaction. Results are presented through the perspective of the Five-Factor Model and

Transactional Distance Theory.

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Chapter 4: Data Analysis and Results

Introduction

The extent of the fit between Five-Factor Model personality traits and

transactional distance within an asynchronous video e-learning environment was not

known. The purpose of this study was motivated by the research gap identified by

Bolliger and Erichsen (2013) in which the researchers recommended investigating the

relationship between personality types and learner interaction satisfaction within

emerging settings. As a result, this research set out to examine the research gap by

addressing the questions of whether or not there is a significant correlation between

personality traits, as measured by the Big Five Inventory (John, 2009), and transactional

distance—a proxy for learner interaction satisfaction—within the video-based e-learning

environment, as measured by the Structure Component Evaluation Tool (Sandoe, 2005),

and which Five-Factor Model personality traits predict transactional distance within the

same environment. The research design was modeled after Kim (2013), which explored

the relationship between personality traits, as well as Kolb learning styles, and academic

performance outcomes following a blended online and in-class communications course.

Data were collected via an online instrument. Participants opened the study

website, completed a pre-course survey that included the collection of demographic

information and the Big Five Inventory (John, 2009), accomplished an online video

course, and provided course perception measurements using the Structure Component

Evaluation Tool (Sandoe, 2005). Upon the conclusion of data collection for the 98

participants, the relationship between personality traits and transactional distance was

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examined. The study employed bivariate correlation analysis and analysis of regression

to analyze the data and answer the following questions:

RQ1: Is there a significant correlation between Five-Factor Model personality traits

and transactional distance within the asynchronous video-based e-learning

environment?

RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning

environment?

The following chapter presents the results of the research. The chapter begins

with a description of the sample population, including basic demographic information. In

order to present specific context to the research, the study environment is then described,

including the data collection methods, timeframes, and response and attrition rates. The

investigation continued by describing the correlational analysis and analysis of regression

methods used to address the research questions. Lastly, this chapter presents the results

of the study, which include a comparison between each of the FFM personality traits and

transactional distance, and an analysis of regression for the significant variables in order

to address the research questions.

Descriptive Data

The population of interest was the U.S. consumer e-learning market, which is the

population of learners interested in short-length courses to develop practical skills. The

population of interest typically accesses learning from home and seeks self-improvement

through online skills-based courses on topics such as communication, relationship

development, computer skills, and finance (LaRosa, 2013). This research utilized an

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online asynchronous video course to deliver three course modules of five to seven

minutes each on the topic of communication within a relationship. All descriptions,

explanations, study instructions, and teaching were conducted using a prerecorded video

in which one actor provided all study-related instructions (e.g., welcome to the study,

instructions for completing the Informed Consent form), and a different actor instructed

the course. Following an introduction to the study, participants completed the electronic

version of the Informed Consent form (see Appendix B) and the pre-course survey (see

Appendix C), which included demographic information and the Big Five Inventory

(John, 2009). Participants then began the communications course. Each course module

required participants to preview course objectives, view the instructional video, respond

to two questions which corresponded to the learning objectives, view a summary of the

lesson, and then continue to the next module. Learner responses to course-based

questions had no bearing on the research and were not measured. Upon completion of

the course, a short video explained the post-course survey, which was the Structure

Component Evaluation Tool (Sandoe, 2005). Upon completion of the SCET, participants

viewed a final video that thanked them for their participation and provided them with

contact information for the researcher in the event the participant had any questions or

concerns.

The study recruited members of the general population interested in self-

improvement to the study website using direct mail and online advertising campaigns

offering a free online communications course for individuals in a relationship as part of a

research study (see Appendix E). The direct mail campaign was organized around the

Town of Gilbert, Arizona, into which 799 advertising postcards were sent to single-

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family residences. Online advertising was conducted using Google Adwords campaigns

directed towards individuals within the United States. The top five most successful

keyword phrases for this campaign that resulted in participants arriving at the Informed

Consent form, which is the fourth page of the instrument, included how to improve

communication skills, communication in a relationship, how to improve my

communication skills, free online communication courses, and how to improve my

communication skills, with an average click-through rate of 2.0%. Overall, the Google

Adwords campaign created 737,091 impressions and received initial responses (clicks)

from 2,798 individuals. A similar campaign was conducted using Facebook social media

paid advertising. The Facebook campaign targeted individuals in the United States

between the ages of 25 and 55 years who are interested in community issues, life, and

relationships. Advertising reached 5,490 individuals and elicited 136 responses, for a

response rate of 2.5%.

The study website registered 1,957 unique IP address hits over the study period of

which 190 unique IP addresses reached the fourth web page of the study, which was the

Informed Consent form and the first research data collection point, and resulted in 158

unique completed Consent forms. The pre-course survey received 158 unique IP address

hits resulting in 151 completed pre-course surveys. The pre-course survey surveyed

general demographic information, including age, gender, marital status, computer and

Internet usage, Internet connection speed, and learning device. The pre-course survey

also included the Big Five Inventory, which measured the participants’ personality traits

of Extroversion, Agreeableness, Openness to Experience, Conscientiousness, and

Neuroticism. The video e-learning course received 208 unique visits, and 125 unique IP

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addresses accessed the post-course survey, resulting in 98 completed post-course surveys.

The post-course survey was the SCET instrument, which measured participants’

perceived transactional distance with the online course. Online advertising, which made

up 99.9% of the total impressions, resulted in a .014% completion per impression rate for

this instrument. Of the 1,957 unique visitors to the study site, 5.2% completed the entire

set of surveys resulting in 98 completed surveys. A summary of study participation and

continuation rates is presented in Table 1.

Table 1 Online Course and Survey Continuation and Completion Data

Impressions Clicks Unique visits

Informed consent Pre-course survey Post-course survey Started Completed Started Completed Started Completed

Count 743,380 2,934 1,957 190 158 158 151 125 98 Continuation rate

- .4% 66.7% 9.7% 83.2% 100.0% 95.6% 82.8% 78.4%

Note. Continuation rate represents the percentage of participants who moved from one activity to the next.

Demographic information gathered about the sample population included age,

gender, marital status, Internet and computer usage, Internet connection speed, and

computing device type and is presented in Table 2. Of the participants, 44 (45%) were

males and 54 (55%) were females. Participants tended to be older than 40 years of age

(76.5%), consisting of ages 40 to 49 years (41.8%), 50 to 59 years (19.4%), 60 to 69

years (6.1%), and 70 years and older (9.2%). Those under 20 represented 4.1% of the

sample population, while ages 20 to 29 years (8.2%) and 30 to 39 years (11.2%) made up

the difference. A majority of participants were married (81.6%), with single (5.1%),

divorced (5.1%), dating (3.1%), engaged (3.1%), widow/widower (1.0%), and other

(1.0%) individuals making up the balance. Participants were regular Internet and

computer users with 77.6% of participants spending six or more hours each week in front

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of a screen, 15.3% spending between three and six hours each week on a computer, 6.1%

spending between one and three hours a week on a computer, and 1.0% spending less

than one hour each week. The majority of participants (89.8%) used a high-speed

Internet connection to complete the course with the remainder (10.2%) using mobile

networks. Laptops (49.0%) were the favored device for participating in the study,

followed by desktop computers (25.5%), phones (16.3%), and tablets (9.2%).

Participant demographic data was entered into SPSS, as were the raw scores for

the BFI and the corresponding SCET values. The 98 participants were screened for

missing personality trait and transactional distance variables. Using mean substitution,

14 missing personality trait values and 22 missing transactional distance values were

replaced with imputed values using the SPSS Replace Missing Values algorithm, which

replaced missing values with the mean value of the valid data points. This method

artificially reduced the variance and standard error of the mean, thus reducing the

opportunity to test for statistical significance. However, mean substitution was preferred

to pairwise deletion, as the deleted datasets would affect 5% or more of the data (Meyers

et al., 2013). Descriptive information, including means, minimums, maximums, standard

deviations, and skewness and kurtosis, was gathered. Table 3 presents the personality

trait and transactional distance measures information for the sample population.

The strongest personality trait, which is measured by the absolute distance from the value

to the midpoint of 50, in the sample was Agreeableness (M = 74.89, SD = 10.92),

followed in order by Conscientiousness (M = 72.04, SD = 13.11), Openness (M = 63.31,

SD = 14.03), Neuroticism (M = 36.70, SD = 15.32), for which the value below the

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midpoint indicates emotional stability, and Extroversion (M = 57.70, SD = 19.92). The

mean SCET score was 11.32 (SD = 5.27).

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Table 2 Participant Demographics (N = 98)

Demographic Category N Percentage Gender Male 44 44.9 Female 54 55.1

Age < 20 years 4 4.1 20 – 29 years 8 8.2 30 – 39 years 11 11.2 40 – 49 years 41 41.8 50 – 59 years 19 19.4 60 – 69 years 6 6.1 70+ years 9 9.2

Relationship Status Single 5 5.1 Dating 3 3.1 Engaged 3 3.1 Married 80 81.6 Divorced 5 5.1 Widow/widower 1 1.0 Other 1 1.0

Internet Usage (weekly) < 1 hour 1 1.0 1 – 3 hours 6 6.1 3 – 6 hours 15 15.3 6 – 9 hours 17 17.4 9+ hours 59 60.2

Internet Connection High Speed 88 89.8 Mobile 10 10.2

Device Desktop 25 25.5 Laptop 48 49.0 Tablet 9 9.2 Phone 16 16.3

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Table 3 Descriptive Statistics of Participant Personality Traits and TD Measures

Variable M SD Skewness Kurtosis Range Minimum Maximum Openness 63.31 14.03 -.16* -.50 60.00 32.50 92.50 Conscientiousness 72.04 13.11 -.28* -.66 58.33 38.89 97.22 Extroversion 57.70 19.92 -.06* -.65 87.50 12.50 100.00 Agreeableness 74.89 10.92 -.27* -.03 52.78 97.22 44.44 Neuroticism 36.70 15.32 -.17* -.69 65.63 3.13 68.75 SCET 11.32 15.27 1.02* -.12 19.75 4.25 24.00 Note. *Skewness exceeds normality range ± 1.0. Tests of linearity and normality. The inferential analysis included tests of

assumptions of linearity and normality across the five FFM personality traits and the

SCET results. A visual inspection was conducted of scatter plots, normal Q-Q plots,

histograms, and box plots, which are presented in Appendix F, and measures of skewness

and kurtosis for each variable. Each of the FFM variables was approximately linear and

normally distributed. Because the skewness of the transactional distance measure

(SCET) exceeded the skewness threshold of ±1.0, the variables were tested for normality

using Kolmogorov-Smirnov and Shapiro-Wilk tests of normality, as presented in Table

F1. The null hypotheses of these tests are that the distributions are normal. Due to the

sensitivity of these tests, a stringent alpha level (p < .001) is required to indicate a

significant result suggesting that the distributions are not normal; a non-significant result

describes a normal distribution (Meyers et al., 2013). Although the tests indicated a

distribution that was not normal, the researcher elected to not adjust or attempt to

normalize the SCET value data in alignment with the central limit theorem, which states,

“As the size of the sample, n, increases, the sampling distribution of the statistic

approximates a normal distribution even when the distribution of the values in the

population are skewed or in other ways not normal” (Fitrianto & Hanafi, 2014, p. 1).

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Test of homoscedasticity. The personality trait variables and transactional

distance variable were tested for homoscedasticity to satisfy the assumptions for

correlation analysis and analysis of regression. The personality trait and SCET values

demonstrated homogeneity of variance across gender. The Levene statistic was

calculated for each variable to measure the homogeneity of variance in which a non-

significant result indicates homoscedasticity and is presented in Table F2.

The sample population was self-selected from the general population to

participate in the research study through responses to advertising. The descriptive data

provided an understanding of the participant attributes, learning methodologies, and

perceived transactional distance. The inferential statistics demonstrated that the FFM

personality trait variables and SCET variables had no outliers, were normally distributed

and linear, and homoscedastic. The variables satisfied the assumptions for Pearson

correlation analysis and regression. The next section describes the data analysis

procedures necessary to address the research questions.

Data Analysis Procedures

This research utilized an online asynchronous video course to deliver three course

modules of five to seven minutes each on the topic of communication within a

relationship. All descriptions, explanations, study instructions, and teaching were

conducted using a prerecorded video in which one actor provided all study-related

instructions, including a welcome statement, a description of the study, instructions for

the informed consent form, instructions for the pre-course survey, instructions for the

post-course survey, and a thank you message, which included the researcher’s contact

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information in the event participants desired follow-up (see Appendix D). The second

actor instructed the three course modules.

Participants entered the website by clicking the link provided in online

advertising, or in the case of individuals responding to direct mail, by typing the study’s

web address into a web browser. The landing page was a video that welcomed

participants to the study, which included a description of the research and of the course.

Following an introduction to the study, participants completed the electronic Informed

Consent form (see Appendix B) and the pre-course survey (see Appendix C), which

included demographic information and the Big Five Inventory (John, 2009). Participants

then began the communications course.

Each course module was between five and seven minutes in length, and required

participants to preview course objectives, view the instructional video, respond to two

questions that corresponded to the learning objectives, view a summary of the lesson, and

then continue to the next module. Learner responses to course-based questions had no

bearing on the research and were not measured. Upon completion of the course, a short

video explained the post-course survey, which was the Structure Component Evaluation

Tool (Sandoe, 2005). Upon completion of the SCET, participants viewed a final video

that thanked them for their participation and provided them with contact information for

the researcher in the event the participant had any questions or concerns.

Participant data was temporarily stored on the research web server. Data was

downloaded daily from the host site throughout the data collection period and formatted

onto a spreadsheet for evaluation of completeness. Data from the pre-course survey and

post-course survey were paired using a unique session-based cookie assigned to the

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participant and recorded with each survey. Data sets in which the participant did not

complete the post-course survey were discarded. The data were then imported into SPSS

for analysis.

Within SPSS, variables were classified by type and measure, and values were

defined. Data sets with five or more blank items for either instrument were discarded.

There were 14 missing personality trait values and 22 missing transactional distance

values with no more than three missing for any given data set. Missing values were

replaced with imputed values using the SPSS Replace Missing Values algorithm, which

utilizes mean substitution, a technique by which the mean value for the given variable of

other data sets is substituted as the missing value. The mean substitution method

artificially reduced the variance and standard error of the mean, which resulted in a

reduced opportunity for statistical significance. This method was preferred, however, to

pairwise deletion, particularly when doing so would affect five percent or more of the

data sets (Meyers et al., 2013). The results of data collection yielded 98 complete data

sets for analysis, which exceeded the minimum sample size of 84 as identified by

G*Power analysis software for a power of .80 and a maximum error probability of .05

based upon an anticipated moderate correlation (r2 = .3) and a two-tailed test (Orvis et al.,

2011; Peng et al, 2012). Because the number of participants exceeds the minimum

sample size, the resulting power (.81) of the study was improved, reducing the probability

of a Type II error (Emerson, 2016).

Personality trait values were calculated within SPSS in accordance with the

instructions for BFI (John, 2009). BFI includes eight to ten items for each personality

trait. For example, trait Extroversion was tested using items 1, 6R, 11, 16, 21R 26, 31R,

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and 36 (see Appendix C). The R suffix represents a reverse scoring of the item as the

item measured Introversion, not Extroversion. For reverse scoring items, values of 1

become a 5 and values of 2 become a 4. The mean for each trait group was calculated

and then statistically normalized to a value between 0 and 100 (e.g., a value of 1 is 0, a

value of 3 is 50, and a value of 5 is 100). Five personality trait values were calculated for

each participant representing each of the FFM traits.

Transactional distance values were calculated within SPSS in alignment with the

instructions for SCET (Sandoe, 2005). The 48 items of the SCET measure eight

subdomains, including overall content organization (7), syllabus (14), course schedule

(5), overall delivery organization (4), delivery consistency (6), delivery flexibility (8),

student-to-student course interaction (2), and student-to-instructor course interaction (2),

with the number in parenthesis representing the number of items used to measure the

subdomain. Subdomain values were calculated as the mean of the related instrument

items, resulting in values ranging from 0 to 3. The SCET value is the sum of the

subdomain values, resulting in values ranging from 0 to 24, with low SCET values

implying wide transactional distance and high SCET values representing small

transactional distance.

Data was screened in SPSS to identify errors, ensure data reliability, and to test

assumptive requirements for follow-on analyses. The descriptive analysis included mean,

standard deviation, range, maximum, minimum, skewness, and kurtosis. Inferential

analysis included examining linearity and normality by visual inspection of scatter plots,

normal Q-Q plots, histograms, and box plots, as presented in Appendix F. No univariate

outliers were found. Skewness and kurtosis were examined to gauge normality. SCET

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was determined to exceed the skewness normality threshold of ±1.0; however, in

alignment with the central limit theorem, the data were treated as a normal distribution

for the sample population (Fitrianto & Hanafi, 2014). The data were examined for

homoscedasticity and were found to exhibit homogeneity of variance for all variables.

The Durbin-Watson test was used to test independence of observations, which were

confirmed, and is presented in Table 7. No collinearity was found as tested by variance

inflation factor (VIF), which is presented in Table F3. The resulting data pairings,

linearity, normality, and homoscedasticity, and the absence of outliers satisfies the

assumptions for Pearson correlation analysis, analysis of regression, and analysis of

variation.

The following additional measures were taken to ensure data validity and

reliability. BFI inventory items were shown to be reliable measures of their respective

personality trait scales with Cronbach’s alphas for each trait scale measuring .75 or

greater as shown in Table 4. Results of personality trait values were compared to U.S.

national averages, as presented in Table 5. The sample population was similar to the

general population with the mean sample value falling within one standard deviation of

the general population (Srivastava, John, Gosling, & Potter, 2003). The research

methodology followed those methods set forth in Kim (2013) and Bolliger and Erichsen

(2013), used validated and reliable instruments, and is replicable through the described

procedures, design, sample collection, and data analyses (Salterio, 2014).

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Table 4 Reliability of Big Five Inventory Scale

Openness Conscientiousness Extroversion Agreeableness Neuroticism Cronbach’s

Alpha .81 .82 .90 .75 .79

Note. Reliability coefficients of .70 or greater indicate acceptable reliability.

Table 5 Comparison of Personality Traits for Sample and General Populations

Population Openness Conscientiousness Extroversion Agreeableness Neuroticism M SD M SD M SD M SD M SD

Sample 3.55 .56 3.88 .52 3.34 .80 4.00 .43 2.46 .62 General 3.92 .67 3.63 .71 3.27 .90 3.73 .69 3.22 .84

Note. Comparison of raw personality trait scores prior to normalizing sample values.

Bonferroni corrections for multiple comparisons were considered for this analysis.

In alignment with the methodology presented by Kim (2013), the researcher did not

adjust the alpha level for Type I errors using an error correction for multiple comparisons

when directly addressing the research questions. The methodology, which utilized an

alpha threshold of p = .05, was consistent with similar research, including Al-Dujaily et

al. (2013), Kickul and Kickul (2006), and Orvis et al. (2011). Correction for multiple

comparisons is not required for testing predetermined hypotheses with a small number of

comparisons, when the multiple usage of simple tests, including Pearson correlation, is

envisaged, and when the results of the individual tests are important (Armstrong, 2014;

Streiner & Norman, 2011), which was the case in addressing the research questions and

the associated hypotheses. By not correcting for multiple comparisons, there was an

increased probability of Type I error; however, the result was beneficial by reducing the

probability of a Type II error (Armstrong, 2014). The Bonferroni correction was

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important and was applicable for instances in which additional ad hoc testing of the data

was used, particularly those instances in which a hypothesis was not developed or tested

(Streiner & Norman, 2011). For those ad hoc tests, such as comparisons of demographic

variables to the SCET values, the Bonferroni corrected alpha was .00625 based upon

eight additional comparisons.

Research Question 1 and hypotheses. The first research question inquired

whether or not there is a significant correlation between personality traits and

transactional distance within the asynchronous video-based e-learning environment.

Pearson correlation analysis was used to address this question by comparing BFI (John,

2009) results for each personality trait measure with SCET (Sandoe, 2005) results. A

significant result indicated that the correlation was significant and rejected the null

hypothesis; a non-significant result failed to reject the null hypothesis. Due to the inverse

relationship between SCET values and transactional distance, a positive correlation

between a personality trait and SCET values represents a negative correlation between

the personality trait and transactional distance. For example, if a trait is positively

correlated with SCET values, then the higher the trait score, the smaller the transactional

distance.

Research Question 2 and hypotheses. The second research question asked to

explain the variance between each personality trait and transactional distance within the

asynchronous video-based e-learning environment. Multiple regression was used to

determine how much of the variance in SCET was explained by each respective trait.

The analysis determined the degree of variance explained by the combination of traits,

allowing the researcher to determine the summative effect of personality traits upon

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transactional distance and allowing greater insight into the role of self-regulatory

constructs upon transactional distance. Hierarchical regression was used to examine the

relationship between individual traits and SCET values in order to address the related

hypotheses. A significant result indicated that the trait was predictive of TD and rejected

the null hypotheses; a non-significant result failed to reject the null hypothesis.

Additional analyses. Independent samples t-tests and ANOVA were conducted

between demographic variable groups and transactional distance to determine if there

were any significant relationships between each of these variables and TD. Those

demographic variables included Internet and computer experience, gender, and device

type. Collinearity was examined for the predictor variables to satisfy assumptions for

linear regression.

Results

To examine the relationship between personality traits, as measured by the Big

Five Inventory (John, 2009), and transactional distance, as measured by Structure

Component Evaluation Tool (Sandoe, 2005), within the online video-based e-learning

environment, correlation analyses were conducted using SPSS. Normalized scores for

each of the five FFM personality traits were used, with possible scores ranging from 0 to

100 for each trait. Each FFM personality trait was independently compared to the SCET

value, which has a possible range of 0 to 24, with 24 representing a small TD and 0

representing a wide TD. In order to accomplish a Pearson correlation analysis, four

assumptions must be met: the variables must be continuous, there must be no outliers, the

data must be linear, and the data must be normally distributed (Meyers et al., 2013).

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Linearity and normality were confirmed via examination of scatter plots,

histograms, box plots, and Q-Q plots, which are presented in Appendix F. Additionally,

normality was examined using skewness and kurtosis, and Kolmogorov-Smirnov and

Shapiro-Wilk tests of normality for each variable, with the central limit theorem applied

to the SCET values. Tests for skewness and kurtosis are presented in Table F1. Each of

these assumptions was met for the variables. Pearson correlation analyses were

conducted between FFM personality traits and SCET. Multiple regression was conducted

for personality trait scores and SCET values. In order to conduct analyses of regression,

the data must conform to the following assumptions: data must be continuous and linear,

the data must not be auto-correlated, the data must be a normal distribution, the data must

not be collinear, and the data must be homoscedastic (Meyers et al., 2013). The previous

section demonstrated that all these assumptions were met, affording the use of analyses of

regression with the personality traits and the transactional distance variable.

Research Question 1 and hypotheses. Research Question 1 asked: Is there a

significant correlation between Five-Factor Model personality traits and transactional

distance within the asynchronous video-based e-learning environment? Using BFI (John,

2009) and SCET (Sandoe, 2005) values to measure personality traits and transactional

distance respectively, Pearson correlation analysis was accomplished. Results of the

Pearson correlation analyses showed that Openness (r = .25, N = 98, p = .02) and

Extroversion (r = .28, N = 98, p = .01) demonstrated statistically significant positive

moderate correlations with SCET scores; thusly, a negative moderate correlation with

transactional distance. Traits Conscientiousness (r = .05, N = 98, p = .65), Agreeableness

(r = .14, N = 98, p = .16), and Neuroticism (r = -.01, N = 98, p = .91) showed no

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significant correlation with SCET values, and as a result, with transactional distance. The

correlation between personality traits and SCET is provided in Table 6.ance

Table 6 Pearson Correlations between FFM Personality Traits and SCET Values

Variable Correlation Sig. (2-tailed) N SCET Openness .25* .02 98 Conscientiousness .05* .65 98 Extroversion .28* .01 98 Agreeableness .14* .16 98 Neuroticism -.01* .91 98 * p < .05.

The results demonstrated that Extroversion and Openness exhibited statistically

significant positive relationships with SCET values, and, therefore, a significant negative

relationship with transactional distance. Specifically, each of the two traits demonstrated

a moderate negative correlation with TD such that as an individual’s manifestations of

Extroversion or Openness increased, the learner’s perceived transactional distance would

decrease with moderate effect. The respective null hypotheses for Extroversion and

Openness in Research Question 1 were rejected, lending valence to the alternative

hypotheses, which stated trait Openness correlates significantly with transactional

distance in the asynchronous video-based e-learning environment and trait Extroversion

correlates significantly with transactional distance in the asynchronous video-based e-

learning environment. In contrast, the relationships between Conscientiousness,

Agreeableness, and Neuroticism were not significantly correlated; therefore, the results

failed to reject the respective null hypotheses.

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Research Question 2 and hypotheses. Research Question 2 asks: Which

personality traits predict transactional distance as explored with regression analysis

within the asynchronous video-based e-learning environment? Using BFI (John, 2009)

and SCET (Sandoe, 2005) values to measure personality traits and transactional distance

respectively, multiple regression analysis was accomplished. The FFM personality traits

were used in a regression analysis to predict the role of all FFM personality traits upon

transactional distance. Regression analysis demonstrated that personality traits were

predictive of TD (F(5, 92) = 3.99, p = .003), the results of which are shown in Table 7.

Hierarchical regression analysis was conducted to determine the roles of each trait

in predicting TD. Separating out the significant variables Openness and Extroversion

enabled the researcher to determine the extent to which each variable independently

predicted the learner outcome variable, SCET values. Independently determining the

predictive capacity of Openness and Extroversion affords evaluation of the relationship

between each personality trait and TD. A three-level model was used, with Extroversion

input as the first step, Openness as the second, and the three remaining, non-significant

trait variables as the third step. It was found that Extroversion and Openness were

predictors of SCET values, and that these two traits explained 14.2% of the variance in

SCET values. Extroversion accounted for 8.0% of variance in SCET scores and

Openness accounted for 6.2% of the variance in SCET scores. The hierarchical

regression analyses for personality traits and SCET are provided in Table 8.

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Table 7 Multiple Regression Analysis of SCET Values by FFM Personality Traits

Model

Unstandardized Coefficients

Standardized Coefficients

t Sig.

Correlations

B SE Βeta Zero- order Partial Part

Constant -7.29 5.61 -1.30 .20* Extroversion .08 .03 .31 3.22 .002 .28 .32 .30 Openness .10 .04 .27 2.76 .007 .25 .28 .26 Agreeableness .09 .05 .18 1.88 .06 .14 .19 .18 Conscientiousness -.004 .04 -.009 -.10 .92 .05 -.01 -.01 Neuroticism .03 .03 .10 .96 .34 -.01 .10 .09 Note. F(5, 92) = 3.99, p = .003, R2 = .18, Adjusted R2 = .13, Durbin-Watson = 1.75.

Table 8 Hierarchical Regression Analysis for FFM Personality Traits and SCET Values

Model Unstandardized Coefficients

Standardized Coefficients

t Sig. B Std. Error Βeta Step 1 Extroversion .08 .03 .28 2.89 .005 Step 2 Extroversion .08 .03 .29 3.00 .003 Openness .09 .04 .25 2.61 .010 Step 3 Extroversion -.080 .03 .31 3.22 .002 Openness -.100 .04 .27 2.76 .007 Agreeableness -.090 .05 .18 1.88 .060 Conscientiousness -.004 .04 -.01 -.10 .920 Neuroticism -.030 .03 .10 .96 .340 Note. R2 = .08 for Step 1; ∆R2 = .06 for Step 2 (p < .05); ∆R2 for Step 3 was not significant (p > .05).

The results demonstrated that Openness and Extroversion were each predictive of

transactional distance within the research environment, with Extroversion predicting

8.0% of the variance of SCET values and Openness predicting an additional 6.2%. As

such, the respective null hypotheses, trait Openness is not significantly predictive of

transactional distance in the asynchronous video-based e-learning environment and trait

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Extroversion is not significantly predictive of transactional distance in the asynchronous

video-based e-learning environment, were rejected. Non-significant results failed to

reject the null hypotheses respectively for Conscientiousness, Agreeableness, and

Neuroticism.

Additional findings in Chapters 4 and 5. The researcher sought to determine

whether or not any relationships existed between the demographic variables and

transactional distance. A total of eight comparisons were made in these ad hoc

examinations. As a result, a Bonferroni correction was made to reduce Type I error. The

following tests require p < .00625 to be considered significant.

An area of interest is the effect of Internet experience upon learners within the

online environment, with some studies showing significant differences between those

experiencing more or less than six hours of Internet usage each week (Siraj et al., 2015).

An independent samples t-test was conducted to compare SCET values in Internet and

computer usage groups that use the Internet for less than six hours each week, and for

those that use the Internet for six hours and greater each week, with no significant

difference in the values for less than six hours per week (M = 11.76, SD = 5.85) and six

hours and greater per week (M = 11.19, SD = 5.12); t(96) = .45, p = .66. Descriptive data

is presented in Table G1. The results, which are presented in Table 9, suggest that

Internet experience did not play a role in learner satisfaction within the video e-learning

environment.

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An independent samples t-test was conducted to compare SCET values across

gender. There was not a significant difference in SCET scores between males (M =

11.87, SD = 5.53) and females (M = 10.87, SD = 5.05); t(96) = .94, p = .35. The

descriptive statistics are presented in Table G2. The results, which are presented in Table

10, suggest that gender did not influence the learner’s transactional distance within the

asynchronous video e-learning environment.

Table 9 Independent Samples t-Test of Internet Experience with SCET Values

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig.

(2-tailed) Mean

Difference Std. Error Difference

95% Confidence Interval of the

Difference Lower Upper

Equal variances assumed

1.021 .32 .45 96 .66 .57 1.28 -1.97 3.12

Equal variances not assumed

.42 30.95 .68 .57 1.38 -2.24 3.38

Table 10 Independent Samples Test of Gender with SCET Values

Levene's Test for Equality of Variances t-test for Equality of Means

F Sig. t df Sig.

(2-tailed) Mean

Difference Std. Error Difference

95% Confidence Interval of the

Difference Lower Upper

Equal variances assumed

1.26 .26 .935 96 .352 1.00 1.07 -1.12 3.13

Equal variances not assumed

.926 88.29 .357 1.00 1.08 -1.15 3.15

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ANOVA was conducted between SCET and device type as a means to determine

whether environmental conditions, such as screen size, may have influenced learner

transactional distance within the asynchronous video e-learning environment. A one-way

ANOVA was conducted comparing SCET values between the four device types of

desktop, laptop, tablet, and phone, the descriptive statistics for which are presented in

Table G3. There was no significant difference between device types as determined by

one-way ANOVA (F(3, 94) = 2.90, p = .04) based upon a Bonferroni corrected alpha.

The results of the ANOVA are presented in Table 11. Tukey post-hoc tests determined a

pre-correction significant result (p = .03) for the SCET means between laptop users (M =

10.19, SD = .58) and tablet users (M = 15.36, SD = 6.94); however, using the Bonferroni

corrected alpha, the result was not significant. The results of Tukey post hoc tests are

presented in Table 12.

Table 11 Analysis of Variation between Device Type and SCET Values

Sum of Squares df Mean Square F Sig.

Between Groups 228.04 3 76.02 2.90 .039 Within Groups 2464.98 94 26.22 Total 2693.02 97 Note. Dependent Variable: SCET; Bonferroni corrected alpha = .00625.

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Table 12 Tukey Post Hoc Test for Device Type Multiple Comparisons

(I) Device (J) Device

Mean Difference

(I-J) Std. Error Sig.

95% Confidence Interval

Lower Bound Upper Bound

Desktop Laptop 1.11 1.26 .82 -2.19 4.42 Tablet -4.06 1.99 .18 -9.27 1.15 Phone -1.13 1.64 .90 -5.42 3.16

Laptop Desktop -1.1 1.26 .82 -4.42 2.19 Tablet -5.17 1.86 .03 -10.04 -.30 Phone -2.24 1.48 .43 -6.11 1.62

Tablet Desktop 4.06 1.99 .18 -1.15 9.27 Laptop 5.17 1.86 .03 .31 10.04 Phone 2.93 2.13 .52 -2.65 8.51

Phone Desktop 1.13 1.64 .90 -3.16 5.42 Laptop 2.24 1.48 .43 -1.62 6.11 Tablet -2.93 2.13 .52 -8.51 2.65

Note. Bonferroni corrected alpha = .00625.

Summary

The purpose of this research was to examine the extent of relationship between

Five-Factor Model personality traits and transactional distance within the asynchronous

video e-learning environment. Chapter 4 provided a description of the data analysis and

results necessary to address the stated purpose. Data analysis included descriptive

information, correlation, and analyses of regression for the sample population of 98

participants.

The descriptive analysis provided insight into the characteristics of the sample

population, including information about personality traits, as measured by BFI (John,

2009) and perceived transactional distance as measured by SCET (Sandoe, 2005).

Descriptive analysis also provided information about the participants’ gender, age,

relationship status, Internet and computer usage, Internet connection type, and type of

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device used to complete the course. Data screening determined that all FFM personality

traits were linear, normal, and homoscedastic. Although SCET values were right skewed,

the distribution was determined to be normal in alignment with the central limit theorem,

and the values resulted in a linear, normal, and homoscedastic data set. Pearson

correlation analyses were used to answer the first research question and analysis of

regression was used to determine the extent of variability due to FFM traits to address the

second research question.

In response to the research questions, results from the Pearson correlational

analyses determined that FFM traits Openness and Extroversion exhibited statistically

significant positive correlations with SCET values, resulting in a significant negative

moderate correlation with transactional distance. Multiple regression demonstrated that

the variance in SCET due to Openness and Extroversion was statistically significant..

The study had a few limitations that may influence the interpretation of the

results. A small number of values for personality traits and SCET scores were imputed

using mean substitution methods, which artificially reduced the potential variance and

standard error of the mean, thusly reducing the opportunity for statistical significance.

Due to the size of the sample population, the distribution of the SCET values was

assumed to approximate a normal distribution despite moderate right skew.

Chapter 4 explained the data analysis steps and study results. The results show

that Extroversion and Openness are positively and moderately correlated with SCET

values, and that personality traits explain some of the variance of SCET values. Chapter

5 summarizes the findings, conclusions, and implications of the research. The final

chapter also offers recommendations for future research and practice.

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Chapter 5: Summary, Conclusions, and Recommendations

Introduction

Learners are more likely to interact with the learning environment, ask questions,

clarify information, and remain open to new information, and, as a result, to perform well

if the learner perceives the environment as satisfying, attractive, and useful (Hauser et al.,

2012; Wang et al., 2014). Moore (1993) introduced three learner interaction types that

occur within the distance-learning environment: learner-learner, learner-instructor, and

learner-content, which describe the learner interaction with learning peers, the instructor,

and media-provided content respectively. Chen (2001) put forward a fourth interaction

type, learner-interface, which described the interaction between the learner and the

learning device. The intensity and quality of these interactions as perceived by the

learner are described as transactional distance, which is the perceived pedagogical gap

between the learner and the learning environment (Ustati & Hassan, 2013). Learner

interactions are influenced by learner self-regulatory processes (Byun, 2014; Wu &

Hwang, 2010), which are related to the learner’s personality traits (Tabak & Nguyen,

2013). Personality traits have demonstrated differing levels of correlation with

transactional distance based upon the specific learning environment, including two-way

video distance learning (Falloon, 2011), hybrid online and in-seat classrooms (Al-Dujaily

et al., 2013; Murphy & Rodríguez-Manzanares, 2008), asynchronous computer-assisted

instruction (Kickul & Kickul, 2006), and game-based learning (Bauer et al., 2012).

The purpose of this quantitative method, correlational study was to address the

gap in the literature identified by Bolliger and Erichsen (2013) to determine the extent of

the relationship between personality traits and transactional distance within the

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asynchronous video-based e-learning environment. Extroversion was previously found to

be related to satisfaction within video environments (Borup et al., 2013; Maltby et al.,

2011; Tsan & Day, 2007), and Agreeableness was previously shown to be correlated with

satisfaction in video conferencing environments (Barkhi & Brozovsky, 2003). The study

of online learners from across the U.S. sought to examine the correlation between

personality traits and transactional distance, and to measure the degree to which learners’

personality traits explained the variance in their ratings of transactional distance. The

study was designed to answer the following research questions: Is there a significant

correlation between Five-Factor Model personality traits and transactional distance

within the asynchronous video-based e-learning environment? Which personality traits

predict transactional distance as explored with a regression analysis within the

asynchronous video-based e-learning environment? The research design was modeled

after Kim (2013), which examined the relationship between personality traits, as well as

Kolb learning styles, and academic performance in a blended online and in-class

communications course.

The extant research did not describe the relationship between personality traits

and transactional distance within the emerging e-learning environment of asynchronous

video. This study provided unique and important insights into the personality trait-

transactional distance relationship for the video e-learning environment, and provides

results that may be considerations for course developers of the growing video e-learning

format. The findings from this research provide additional information describing the

relationship between learner personality traits and transactional distance within emerging

e-learning environments and reveal opportunities for future practices and research.

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Chapter 5 provides a comprehensive synopsis of the study, a summary of the findings and

conclusions, recommendations for future research and practice, and implications of this

study.

Summary of the Study

The purpose of the study was to examine the relationship between FFM

personality traits and transactional distance within the asynchronous video e-learning

environment. The study was significant as it addressed the gap in the literature identified

by Bolliger and Erichsen (2013), which stated the need to examine the relationship

between personality traits and learner interaction satisfaction within emerging learning

environments. Relationships between personality traits and learner outcomes had been

identified in a variety of environments, including two-way video distance learning

(Falloon, 2011), hybrid online and in-seat classrooms (Al-Dujaily et al., 2013; Murphy &

Rodríguez-Manzanares, 2008), asynchronous computer-assisted instruction (Kickul &

Kickul, 2006), and game-based learning (Bauer et al., 2012). However, there was no

research exploring the extent of the fit between personality traits and transactional

distance within the asynchronous video e-learning environment. The ways in which the

study advanced scientific knowledge on the topic of personality in the e-learning

environment were also discussed, including identifying those personality traits that

demonstrate a fit with the online video-based learning environment (Bolliger & Erichsen,

2013), further developing the construct of perceived dialogue (Maltby et al., 2011), and

bringing greater focus on the relevancy of self-regulatory processes within learning

interactions (Moore, 1993). The study also advanced discussion points regarding

personality trait theory as it relates socially based traits, such as Extroversion, to

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technology-based environments (Al-Dujaily et al., 2013; Borup et al., 2013; Orvis et al.,

2011; Tsan & Day, 2007). A description and explanation for the methodology and

research design were also described.

An exhaustive review of seminal and contemporary literature related to

personality traits and learner interactions was accomplished. Some key areas discussed

in the literature review were constructivist characteristics of learning through active

learning (Lucas et al., 2013; Thomas & Macias-Moriarity, 2014) and learning styles

(Bhatti & Bart, 2013; Black & Kassaye, 2014; Chen et al., 2014), learner interactions

across a broad spectrum of learning environments, including traditional (Byun, 2014;

Gosling et al., 2011; Rodríguez Montequín et al., 2013), online (Al-Dujaily et al., 2013;

Bauer et al., 2012; Chang & Chang, 2012; Kickul & Kickul, 2006; Orvis et al., 2011),

and hybrid environments (Gross, Marinari, Hoffman, DeSimone, & Burke, 2015;

McCallum et al., 2015; Moffett & Mill, 2014; Velegol et al., 2015), and the examination

of psychological constructs within the learning environment (Batey et al., 2011; Caprara

et al., 2011; Hetland et al., 2012; Wu & Hwang, 2010) and the relationship of those

constructs with learner outcomes (Kim, 2013). The literature review thoroughly

reviewed the relationship between personality traits and online learning environments

(Gosling et al., 2011; Hertel et al., 2008; Killian & Bastas, 2015; Threeton et al., 2013),

and revealed a gap in the literature in describing the relationship between personality

traits and learner outcomes in asynchronous video-based online learning environments

(Bolliger & Erichsen, 2013). The literature was then used to describe the methodology

applicable to personality trait and learner interaction research (Blignaut & Ungerer, 2014;

Opateye, 2014; Pretz & Folse, 2011; Reyes et al., 2015), as well as an examination of the

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instrumentation suitable for exploring the relationship between the two constructs (Chen,

2001; Costa & McCrae, 1995; Dwight et al., 1998; Feldt et al., 2014; Horzum, 2011;

Huang, 2002; John, 2009; John & Srivastava, 1999; Sandoe, 2005).

Chapter 3 stipulated the methodology and research design appropriate for the

research and described how the methodology and design supported the purpose of the

study. Study participants completed a pre-course survey, which included six

demographic questions and the Big Five Inventory (John, 2009) to measure personality

traits, and then accomplished the asynchronous video e-learning course. Participants then

completed the Structure Component Evaluation Tool (Sandoe, 2005) to measure

transactional distance. The process for data collection and analysis were detailed in order

to address the following research questions.

RQ1: Is there a significant correlation between Five-Factor Model personality

traits and transactional distance within the asynchronous video-based e-learning

environment?

RQ2: Which personality traits predict transactional distance as explored with

regression analysis within the asynchronous video-based e-learning environment?

Data analysis procedures and the results of the study were detailed in Chapter 4.

Descriptive and inferential statistics were displayed and assumptions for correlational

analysis and analysis of regression were supported. Pearson correlational analysis was

used to determine the relationship between personality traits and values from SCET, and

heirarchical regression was accomplished to determine the extent of the fit between

personality traits and transactional distance. The results of the analyses were used to

answer the research questions and hypotheses.

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Chapter 5 summarizes the study and examines the key results from the data

analysis. The present chapter continues by discussing the findings and conclusions from

the results to inform theoretical and practical implications, and presents recommendations

for future research.

Summary of Findings and Conclusion

This section of Chapter 5 provides a summary of findings from the data analysis

presented in Chapter 4. The summary of findings begins by addressing the research

questions and hypotheses that framed this study and then presents the findings. The

section will then provide conclusions based upon the results and will relate these

conclusions to current research on personality traits and learner interaction within the e-

learning environment and the research’s significance to scientific knowledge.

Research Question 1 and hypotheses. The first research question examined

whether or not a relationship existed between personality traits and transactional distance

within the asynchronous video e-learning environment, with an alternative hypothesis

that there would be a statistically significant correlation between the two variables as

measured by BFI (John, 2009) and SCET (Sandoe, 2005). The results showed that

Openness (r = .25, N = 98, p = .02) and Extroversion (r = .28, N = 98, p = .005)

exhibited statistically significant positive moderate correlations with SCET values, which

is a significant negative moderate correlation with transactional distance; thereby the

results rejected the null hypotheses and provided support for the alternative hypothesis

that there is a correlation respectively between the two variables. The results of

correlation analysis were not significant in the respective relationships between

Conscientiousness (r = .05, N = 98, p = .65), Agreeableness (r = .14, N = 98, p = .16), and

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Neuroticism (r = -.01, N = 98, p = .91), and SCET values, thusly failing to reject the null

hypotheses that such correlations did not exist.

These findings support contemporary literature, which described a positive

correlation between Extroversion and learner satisfaction within various video-based

interaction environments (Borup et al., 2013; Tsan & Day, 2007). This study’s

investigation of the personality trait-learner interaction relationship advanced previous

research by adding to the knowledge about personality traits and learner outcomes, a gap

identified by Bolliger and Erichsen (2013) by showing that learners with higher levels of

Extroversion and Openness perceived a closer communication and psychological

connection to the learning environment compared to learning peers with lower

manifestations of those traits. Additionally, the research highlighted the involvement of

self-regulatory processes in influencing learner interaction, which added additional

understanding to Moore’s (1993) Transactional Distance Theory. The research also

added to the construct of celebrity attitudes, in which individuals are attracted to

individuals who are on-screen (Maltby et al., 2011).

Specifically, the present research showed that learners with increased

Extroversion developed a closer sense of interaction and comfort with the video learning

environment. Extroversion facets of Activity, Excitement Seeking, and Positive

Emotions may in part explain the relationship between Extroversion and transactional

distance within the asynchronous video e-learning environment (McCrae & Costa, 2003).

Individuals exhibiting higher levels of Extroversion prefer stimulating environments,

such as settings with activity, variety, and diversity, that arouse the individual

emotionally and psychologically. High activity environments create a strong level of

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excitement within the extroverted individual, an environment that keeps his or her

attention as the individual seeks to maintain or increase the level of arousal, which is

viewed by the individual in a positive manner. In contrast, individuals with low

Extroversion scores, or introverts, tend to feel overwhelmed by high levels of

environmental stimulation and, as a result, tend to distance themselves from the

environment (Hertel et al., 2008). The increased activity of the environment creates a

negative psychological effect within the introverted individual, decreasing the

individual’s affinity for the setting. Simply put, the environment has so much going on

that the introverted individual in unable to process all the activity at the pace in which it

is presented and it makes the individual uncomfortable. The video environment, with its

consistent and persistent pace, and with its visual and audible stimuli (Stigler et al.,

2015), provides an exciting, active, and diverse environment for the extrovert while at the

same time providing an overwhelming, frenetic, and uncomfortable learning setting for

the introvert. As a result, extroverts will draw closer to the video learning environment

while introverts will tend to push it away.

Although the literature had not described a relationship between Openness and

learning outcomes or individual satisfaction within video environments, learners scoring

higher along the Openness dimension are more willing to engage in learning when

exposed to new learning environments (Orvis et al., 2011). Openness represents the

individual’s willingness to try new activities, to experience new formats, and to consider

novel ideas and values (McCrae & Sutin, 2009). As a consequence, it seems reasonable

for an individual with strong Openness characteristics to enjoy a novel environment more

than an individual with lower Openness characteristics. Although video media has been

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used in learning applications for decades within the in-seat classroom (Ljubojevic et al.,

2014), and it has become ubiquitous in contemporary life through mobile applications

(Park, 2011), the use of asynchronous video exclusively for online instruction is a

relatively recent occurrence (Docebo, 2014). As such, asynchronous video represents a

break from traditional instructional settings and requires the learner to adapt to the new

format. Adapting to a new environment, specifically the asynchronous video e-learning

environment, understandably comes more naturally for those learners higher in Openness

than those individuals measuring lower on the Openness scale, and as a result, learners

higher in Openness are more comfortable with the environment and develop a stronger

sense of interaction with the environment. It is noteworthy that Openness and

transactional distance do not appear to be related in more traditional learning

environments, such as in-seat classrooms (Furnham, 2012), computer-aided instruction

(Al-Dujaily et al., 2013), and blended learning environments (Kim, 2013), which may be

due to learners being familiar with these long-standing environments and settings, which

lack any sense of novelty. As a result, it is possible that as asynchronous video e-learning

becomes more universal and less novel, it may be expected that the strength of the

relationship between Openness and transactional distance within the video learning

environment would diminish with the decreasing novelty of the environment. Likewise,

as new environments emerge or as significant changes occur within more established

environments, one would expect that transactional distance would be related to learner

Openness.

Research Question 2 and hypotheses. The second research question examined

the extent to which personality traits explained the variability of transactional distance

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within the asynchronous video e-learning environment. Hierarchical regression analysis

showed that Openness and Extroversion demonstrated a statistically significant degree of

prediction of transactional distance, with Extroversion predicting 8.0% of the variance in

SCET values (p = .005) and Openness predicting an additional 6.2% of the variance in

SCET values (p = .01). Results of regression analyses for Conscientiousness,

Agreeableness, and Neuroticism were not significant (p > .05) and indicated these traits

were not predictive of SCET values. The null hypotheses stating that both Openness and

Extroversion were not significantly predictive of transactional distance in the

asynchronous video-based e-learning environment were rejected. The null hypotheses

were not rejected for Conscientiousness, Agreeableness, and Neuroticism.

Moore’s (1993) Transactional Distance Theory describes that self-regulatory

processes play a role in determining the transactional distance experienced by a learner

within an environment based upon the factors of dialogue, learning structure, and learner

autonomy. The analysis of regression supported Moore’s assertions by describing that

8.0% of the transactional distance within this research environment was explained by

Extroversion and that 6.2% of the transactional distance within the asynchronous video

environment was explained by Openness, both of which are members of a self-regulatory

construct. Extroversion and Openness demonstrated no correlation with one another and

each trait contributed uniquely to the transactional distance with no covariance. As a

result, Extroversion and Openness jointly predicted 14.2% of the variance in transactional

distance.

The results advance the construct of personality, a form of self-regulation, as an

influencer of transactional distance (Moore, 1993; Ustati & Hassan, 2013). Previous

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research focused upon the three design factors of dialogue, learning structure, and learner

autonomy as being most influential in determining transactional distance (Chen, 2001;

Chen & Willits, 1998; Papadopoulos & Dagdilelis, 2007). Instead of considering a single

variable group, such as design variables, as determining transactional distance,

contemporary studies examine transactional distance as a function of two variable

groups—design variables and the learner’s self-regulatory constructs.

The present results, in combination with other similar studies (e.g., Bolliger &

Erichsen, 2013; Falloon, 2011; Hauser et al., 2012), demonstrated that within the same

environment under the same conditions, an individual’s perceptions of transactional

distance vary based upon the learner’s psychological constructs. These variances occur

due to the unique psychological make-up of each learner. Personality traits, for example,

describe the behavior an individual is likely to exhibit within given circumstances.

However, because each person responds differently to circumstances, the individual is

said to have different traits, which is a fundamental characteristic of personality trait

theory (Soto & John, 2012). Although traditional literature discussing transactional

distance theory focused upon the design factors of a learning environment, contemporary

literature, along with this study, advances the idea that the individual’s response to design

factors also plays a measureable role in influencing TD. In fact, this study’s results were

that personality traits predicted 18% of transactional distance measures, F(5, 92) = 3.99,

p = .003. The learner’s response to dialogue, learning structure, and learner autonomy is

unique based upon self-regulating constructs and is significantly influential in

determining the perception of transactional distance, which is in contrast to the

historically singular focus on the design elements of Moore’s (1993) TD factors. The

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results also indicated that not all self-regulatory functions show influence within a given

environment, as demonstrated by Conscientiousness, Agreeableness, and Neuroticism.

The results support the Five-Factor Model of personality tenet that personality

traits measure unique individual characteristics with little or no correlation between traits

(Paunonen, 2003; Thalmayer et al., 2011). The results also support FFM’s assertion of

the independence of personality traits. FFM traits are described as being independent and

not collinear, affording each personality trait to describe a separate and unique behavioral

set (Paunonen, 2003; Thalmayer et al., 2011), which is described by the results of this

study with Openness and Extroversion exhibiting influence, and Conscientiousness,

Agreeableness, and Neuroticism not explaining the outcome in a significant manner.

Although the results from the sample population indicated modest collinearity between

Openness and Neuroticism, only Openness demonstrated a significant relationship with

transactional distance.

Additional findings in Chapters 4 and 5. Demographic variables reflected no

significant influence upon transactional distance. The lack of a significant relationship

between Internet and computer usage and transactional distance was in contrast to the

findings of Siraj et al. (2015). Compared to the relatively even levels of transactional

distance based upon Internet usage within the present study, Siraj et al. stated that

individuals that spend six or greater hours on the Internet each week exhibited higher

learning outcomes. Differences between the two studies may be attributable to the

participant selection methods used in the respective studies. Siraj et al. examined the

effect of computer experience upon online learning results within a semester-long

medical school course, an environment in which learner participants may or may not have

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been familiar with utilizing computers. The present research recruited participants via

the Internet, which may suggest that the learners were comfortable enough with the

learning environment to voluntarily participate in the online course regardless of their

computer and Internet experience.

The relationship between the learner interface, which was the device type, and

SCET values was investigated, and resulted in a non-significant result. Chen (2001)

suggested that the learner interface interaction was a factor in perceived transactional

distance and that the device type may have influenced the results. Because the

participants were able to choose the device type, it is likely each learner selected the

device with which they were most comfortable, thusly mitigating any differences in TD

due to device type.

The findings of this research were that Openness and Extroversion was negatively

and moderately correlated with transactional distance within the asynchronous video e-

learning environment, rejecting the null hypothesis of the first research question. The

findings were that Openness and Extroversion were significantly and independently

predictive of TD within the research environment, and rejected the respective null

hypotheses. The results showed Extroversion explained 8.0% of the TD measure and

Openness predicted an additional 6.2%. These results advanced scientific knowledge in

the area of personality traits within video environments, e-learning environments, and the

potential for influence on perceived dialogue. Lastly, it was found that personality traits

as a whole explained 18% of transactional distance measures, a result that informs the

scientific community of the role self-regulatory processes within Transactional Distance

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Theory. The next section discusses the theoretical, practical, and future implications of

these results.

Implications

The results of this research have made a contribution to the advancement of

scientific knowledge on the topic of personality traits and transactional distance within

the asynchronous video e-learning environment. The following section examines the

theoretical, practical, and future implications of the research. Theoretical discussions

include the increasing role of self-regulatory processes, namely personality traits, as a

factor for consideration within Transactional Distance Theory. The practical implications

section addresses personality trait considerations for learning design. Future implications

include how the results fill in a gap within the extant literature and how this increased

understanding of the e-learning topography strengthens the opportunity for the

development of a personality trait-based theory for e-learning.

Theoretical implications. Moore’s (1993) Theory of Transactional Distance

states that the perceived communication and psychological distance between a learner

and the learning environment is a function of three factors: dialogue, learning structure,

and learner autonomy. Traditionally, TDT research focused on finding the optimal levels

of each factor in order to garner shorter transactional distances and to produce the

greatest learning outcomes (Benson & Samarawickrema, 2009). The traditional approach

was that the factor, such as dialogue, was an independent variable that influenced the

dependent variable of transactional distance (Wang & Morgan, 2008; Zhou, 2014). More

recently, there has been a shift towards examining the role of the learner’s characteristics

within individual environments. This shift explores the question of which types of

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learners, based upon self-regulatory processes, will be more likely to succeed in a

specific environment (Bauer et al., 2012; Kickul & Kickul, 2006; Moffett & Mill, 2014).

The change in experimental focus from the role of structural elements within learning

environments upon transactional distance to the role of self-regulatory constructs in

determining transactional distance is a major adjustment. The present research supports

the change in focus towards self-regulatory processes. As this research revealed,

personality traits are a significant contributor towards the learner’s perception of his or

her satisfaction with a learning environment’s pedagogy. The stronger a learner’s

Extroversion or Openness trait, the more the learner narrows the communication and

psychological distance within the asynchronous video e-learning environment.

The research also offered support for the Five-Factor Model of personality traits.

The descriptive data of participants’ personality traits included the mean values for each

trait. The Big Five Inventory, a validated, reliable, and widely-used instrument, was used

to measure the personality traits of participants (John, 2009), and offered a high level of

validity and reliability to this study. The personality trait mean values for the sample fell

within one standard deviation of U.S. values (Srivastava et al., 2003), which offers

support for the generalizability of Five-Factor Model of personality traits.

The research also supports the premise that personality traits offer unique

behavioral responses to a stimulus (McCrae & Costa, 2003). The results indicated that

the behavioral response, which was perceived transactional distance, was unique based

upon only personality traits Openness and Extroversion. These are differing responses to

other similar research in which Agreeableness was related to satisfaction within video

conferencing environment (Barkhi & Brozovsky, 2003) and where Extroversion and

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Conscientiousness were related to learner outcomes in traditional classrooms (Kim,

2013).

Lastly, the results support the methodology that the preferred determinant for

one’s personality is observation and measurement of behavioral outcomes instead of self-

report instruments (Feldt et al., 2014). Although a modest negative correlation existed

within this study between the self-reported measures for Openness and Neuroticism, the

behavioral outcome, which is the TD measure, aligned with each trait was not similar.

Previous research described the observed measure of personality is a more accurate

determinant of a facet or trait when compared to self-reported measure (Feldt et al., 2014;

Gosling et al., 2011). In the present study, Openness showed a significant relationship

with TD while Neuroticism exhibited no relationship with TD despite the statistical

correlation between these two traits, suggesting that an individual’s actual behaviors may

be different than the individual’s perceived or anticipated behaviors.

Practical implications. Based upon the results of this study, some suggestions

can be made about personality traits and learner outcomes within video-based learning

environments. The results indicated a non-significant result for the relationship between

Agreeableness and TD within the research environment. This result was in contrast to

the results of Barkhi and Brozovsky (2003), which found a significant correlation

between Agreeableness and individual satisfaction within video conferencing

environments. Although both environments utilized video to present information, there

was a notable difference between the asynchronous video e-learning environment and

video conferencing; namely, the former was an independent, asynchronous video

presentation and the latter was a social, synchronous video environment. The non-

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significant result may offer some insights into the role that Agreeableness plays in

differentiating between the social aspects of technology environments. Within the video

conferencing environment, the individual interacts with others through synchronous,

multi-directional communication. Falloon (2011) noted that although some learners

enjoyed the video interactions with instructors and peers, others did not enjoy the

environment and were reluctant to participate. On the other hand, the present research

environment was a content-driven, highly structured video environment without social

interaction. The difference between the environments is applicable as Agreeableness

denotes social tendencies and is defined as the selfless concern for others (McCrae &

Costa, 2003). Within environments void of social interaction, it is possible that this trait

does not have the opportunity to manifest itself and, therefore, it plays only a minor role

in determining perceived pedagogical, communication, or psychological closeness (Ustati

& Hassan, 2013).

Openness was significantly related to transactional distance within the research

environment, a result that lends practical applications for course development

stakeholders. The characteristics of Openness include openness to actions, which is the

willingness to try new things (McCrae & Costa, 2003). Video environments offer the

possibility to provide a variety of visual and audio stimulus (Vural, 2013), which may

keep the learning environment fresh and appealing to individuals high in Openness,

maintaining a high level of interaction satisfaction and a similarly small psychological

and pedagogical distance. A contrasting explanation from the literature is that learners

exhibiting less Openness were associated with a lack of self-regulation and had few to no

use of learning processing strategies, while those showing higher levels of Openness

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demonstrated the use of learning strategies (Donche et al., 2013). A characteristic of

Openness is that open people are curious and think of a broad array of possibilities, and

subsequently display discernment between possibilities (McCrae & Costa, 2003).

Openness is associated with deep and concrete processing, and self-regulation (Donche et

al., 2013), characteristics that may be facilitated by the structured nature of asynchronous

video. Both explanations support that individuals high in Openness participating in

asynchronous video instruction should experience small transactional distance.

Alternatively, this line of thought suggests that if learners low in Openness participate in

video instruction, they would prefer video learning environments that are consistent and

predictable.

The results indicated that personality traits predicted 18% of the SCET measure

within the asynchronous video e-learning environment with Extroversion predictive of

8.0% of SCET measures and Openness predictive of 6.2%. Two significant implications

arose from this relationship. First, Extroversion was the most influential personality trait

in determining transactional distance within the asynchronous video e-learning

environment. As a result, curriculum developers who are considering personality traits

when designing a course should first consider Extroversion as the primary trait

consideration. For example, if developing a course that is known to attract extroverted

individuals, designers should consider an asynchronous video environment, as learners’

with high measures of Extroversion tend to display small transactional distance within

these environments. For the present study, a change of one standard deviation of

Extroversion measure (M = 57.7, SD = 19.9) represents a change of 1.6 (β = .08) out of a

possible 24 on the SCET scale, which is a 6.6% change in perceived transactional

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distance. These results suggest that an individual scoring 77.6 on the Extroversion

measure would perceive a closer transactional distance by 6.6% over the average learner.

Likewise, designers developing a curriculum that is likely to attract learners with lower

Extroversion measures should grant consideration to other learning environments. A

second option for developers of courses with introverted learners is to provide a paced,

low activity environment with minimal interaction with the interface to allow the

introverted learner the ability to reflect on the material throughout the course, a

recommendation supported by Orvis et al. (2011).

The second consideration is the learner’s experience within the video e-learning

environment. As previously discussed, learners with high measures of Openness prefer

novel environments (McCrae & Sutin, 2009). However, as the novelty of the video

environment wanes, there is the potential for the influence of Openness upon TD to

subside as well. Although Openness is a prominent consideration for current curriculum

development, predicting 6.2% of TD within the asynchronous video environment, this

influence may decrease as the learning format becomes more ubiquitous, suggesting that

developers may need to focus upon other characteristics.

Future implications. The literature described attempts to develop a working

model to inform learning design using Transactional Distance Theory as a significant

factor within distance learning and mobile environments (Benson & Samarawickrema,

2009; Park, 2011). These models focus upon the relationship of the environmental

characteristics, such as the level of available dialogue, the learner’s role in determining

learning outcomes, and the structure around which the learning environment was created,

with transactional distance, and treat the learner as a homogenous unit of measure. More

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recently, the literature recognized the uniqueness of individuals and described a variety of

associations between personality traits and learning environments: Al-Dujaily et al.

(2013) described a relationship between Extroversion and computer-assisted instruction;

Bauer et al. (2012) showed that Openness and Neuroticism correlated with task difficulty

conditions in game-based learning; and Orvis et al. (2011) provided results that training

performance was higher for trainees with higher expressions of Openness and

Extroversion. The present research showed that Openness and Extroversion related to

stronger and higher quality interactions within the asynchronous video e-learning

environment.

The growing scope of literature relating personality traits with transactional

distance within electronic environments affords the opportunity for newer, descriptive

models that describe past results and that predict future personality trait-learner outcome

relationships to be developed. Whereas past models created a two-by-two matrix

comparing an environment’s structure and dialogue opportunities (Benson &

Samarawickrema, 2009; Park, 2011), a more detailed model would include a third

dimension relating self-regulatory constructs, including personality traits. Such a model

would explore the relationships between personality trait facets and learning environment

characteristics in order to create a predictive and validating framework for outcomes

within e-learning environments.

Strengths and weaknesses. The present research exhibited a number of strengths

that are noteworthy. The study utilized quantitative methodology and correlational

design, which afforded the use of statistics to compare measurable variables in order to

establish the veracity of relationships (Arghode, 2012) and was modeled after Kim

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(2013), which examined the relationship between personality traits, as well as Kolb

learning styles, and academic performance within a blended online and in-class

communications course. By employing quantitative methods, this study provided a

duplicable procedure by which the study and its results can be measured and compared,

and a standardized set of results that are useful for comparison to other research (Wallis,

2015). All assumptions for correlation analysis and analysis of regression were tested

and confirmed as valid for the data set using descriptive and inferential statistics.

Pearson correlation and analysis of regression were then conducted to address the

research questions and to reach results that allowed the judgments of the hypotheses

based upon objective data and clear criteria.

A concern prior to testing was the ability to ensure a normal distribution of

personality traits across the sample population. This limitation was alleviated when

descriptive and inferential statistics indicated a normal distribution for all FFM

personality traits, and that the mean of the traits were within the boundaries of national

averages (Srivastava et al., 2003). The current research utilized standardized, validated

instruments to measure the variables in order to determine whether or not statistically

significant relationships existed, and did not attempt to draw conclusions about the cause

of such phenomena. The Big Five Inventory (John, 2009) is a validated and reliable

instrument for measuring personality traits and is considered a standard against which

other personality tests are measured (Thalmayer et al., 2011). Its use lends credibility to

the data collected as being accurate and reliable.

In contrast, the availability of instruments for measuring transactional distance

was a weakness. Measurement of transactional distance has proven difficult with

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instruments having been developed to address specific learning environments (Horzum,

2011). As a result, selecting an instrument for an emerging environment was a matter of

matching a test’s characteristics to the expected learning environment. The Structure

Component Evaluation Tool (Sandoe, 2005) was developed for the purpose of measuring

the structure of online learning environments similar to the online courses found at major

universities. This environment was compared to hybrid environments (Horzum, 2011)

and interactive online environments (Chen, 2001; Huang, 2002) for which instruments

had been developed. In the end, SCET was determined to be the best match for the

asynchronous video e-learning environment due to the highly structured nature of the

research environment; however, the match for the environment was not perfect. The

course within the research instrument was intended for the consumer learning market,

with short course modules and no formal instructor or learning peers, whereas the SCET

instrument measured the learner perceptions of a syllabus and interaction with peers and

faculty. Most participants rated these areas with a 0, which was that these areas were not

evident, resulting in results that were right skewed (1.02) due to the lower overall SCET

scores. An instrument appropriate for measuring transactional distance within consumer

level e-learning focusing on the areas of content, organization, consistency, flexibility,

and availability would be more suitable when developed. This weakness was addressed

as a delimitation. This weakness, however, exposed structural elements that are different

between typical consumer-level courses, such as this study’s learning environment, and

formal academic curriculum; namely, a syllabus, peer-to-peer communication, and

learner-to-instructor communication.

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An additional delimitation of the study still in force is that the study addressed the

specific self-regulatory construct of personality traits and did not consider other

constructs, such as motivation, self-efficacy, or anxiety (Hauser et al., 2012). The model

presented by Hauser et al. (2012), however, suggested that at least some of these

constructs—namely anxiety and self-efficacy—influenced learner outcomes as a function

of transactional distance and not as factors that affect TD. The research did address the

construct identified through FFM personality traits, and described that Extroversion and

Openness were related to and predictive of transactional distance.

Lastly, a weakness of the study was that it was limited to consumer-level video e-

learning. Transactional distance describes the perceived relationship that developed

between a learner and the learning environment, whether it is an instructor, other learners,

the content, or the interface (Chen, 2001; Moore, 1993). Within the literature, these

relationships are described and measured over the course of medium to long periods of

time, such as a semester-long course. The length of time to complete the study

instrument was between 30 and 40 minutes, which is a stark contrast to the traditional

course. As a result, the ability to develop a psychological bond between the learner and

learning environment was limited in time, which may have affected the results. With this

limitation in mind, the results may not be generalizable to all asynchronous video e-

learning courses if the course length is substantially longer than that of this research

study. However, the results are still applicable to short-length courses, similar to those

found on commercial websites such as Lynda.com, KhanAcademy.com, Vimeo.com, and

YouTube.com, which feature highly-focused, skills-based training for online consumer-

learners.

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Recommendations

An axiom of research is that research results bring about new questions and

considerations for future research, and this research is no different. The results of this

investigation into the extent of the relationship between personality traits and

transactional distance in the asynchronous video e-learning environment bring forth a

number of considerations for researchers investigating topics related to this study. The

following sections provide recommendations for future research and practice.

Recommendations for future research. As previously stated, the choice of

instrument for measuring transactional distance required the researcher to estimate which

instrument would be the best fit for the asynchronous video learning environment.

Although the results using SCET (Sandoe, 2005) were valuable, valid, and reliable, it is

recommended that the study be repeated using a different TD instrument, such as Student

Perceptions of Online Courses (SPOC) (Huang, 2002). Whereas SCET focuses upon the

structural elements of a course in determining the transactional distance between the

learner and the learning environment, SPOC places relatively equal emphasis upon four

factors: dialogue, structure, learner autonomy, and learner interface. SPOC was

developed for blended learning environments and addresses the involvement of a

classroom instructor, which is why it was not initially considered. However, some SPOC

instrument items expand on the idea of learner autonomy and learner interface, such as I

am able to direct my own learning and I believe the Internet provides an efficient way for

interactive learning, directly addressing areas that were minimized or excluded from

SCET. It is expected that such a study would achieve similar results as the present study,

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but would do so based upon a more equally weighted look at the transactional distance

factors.

A second recommendation is to examine the role of personality traits in

influencing transactional distance within the asynchronous video e-learning environment

through a longer course length. The present research was designed with the intent of

examining the consumer video-based e-learning market, which is comprised of self-

improvement skills courses consisting of short-length modules (LaRosa, 2013). In order

for the results to be generalized to the traditional education market, the courses used

within the research would need to replicate the extended length and structure of the new

environment. As has been shown throughout the literature, changes within the learning

environment, whether delivery method (Buch et al., 2014), dialogue structure (Borup et

al., 2013; Wang & Morgan, 2008) or level of autonomy (Orvis et al., 2011), can influence

the perceived transactional distance. The recommended study would extend the length of

the course, increase the structure, and increase the dialogue opportunities, influencing the

design-based transactional distance factors (Benson & Samarawickrema, 2009; Moore,

1993; Park, 2011). The benefits of the recommended study include applications of the

knowledge to the traditional learning population, such as online universities, and

extending the information available from which to develop a comprehensive model

describing the relationships between transactional distance and self-regulatory constructs.

A third recommendation is to investigate the extent that personality traits play a

role in determining transactional distance within different video modalities. The present

study utilized studio-recorded instruction where the instructor looked directly at the

camera. The instructor used personal anecdotes and spoke to the learner directly using

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casual language to develop rapport. It has been shown that video strategies that promote

relationship building between the learner and the video instructor positively affect learner

attitudes and learning gains (Kim & Thayne, 2015). Also included in the instruction were

short animated sequences to provide vignettes as examples and video b-roll—video of

supporting scenes as the instructor speaks off camera—to maintain visual interest. Video

modalities suitable for examination in future research include classroom-recorded lecture,

studio-recorded lecture, and a faceless narrator that describes onscreen material. In a

case study of flipped classrooms, Velegol et al. (2015) used classroom-recorded and

studio-created video in an examination of flipped classrooms; however, the study did not

specifically address learner preferences or learner outcomes as a result of the video

format. Additionally, the recommended video formats are similar to the video

presentations described in Broadbent et al. (2013) in which patients preferred a medical

robot with a face on its display, rating it as being most alive, sociable, and amiable, over

a medical robot with no display face. Understanding the relationship between video

modalities and transactional distance as moderated by personality traits would be

beneficial in guiding curriculum development and educational video production.

A fourth recommendation is to determine the extent of relationship between

transactional distance and learning performance as moderated by personality traits. The

present research determined that Extroversion and Openness were negatively and

moderately correlated to transactional distance within the research environment, which is

related to the learner’s level of satisfaction within the environment. Orvis et al. (2011)

found that Extroversion and Openness moderated the relationship between learner

control, which is a factor of transactional distance, and learning performance. The next

199

step is to determine whether or not the combination of transactional distance and

personality yield any insights into the learning performance of the online student.

A fifth recommendation is to determine the factors within the asynchronous video

e-learning environment that uniquely influence specific personality traits. The present

research demonstrated that Extroversion and Openness were negatively and moderately

correlated with TD within the research environment; however, the two traits were not

correlated with one another, suggesting that individuals with high levels of Openness

were not necessarily the same individuals that were high in levels of Extroversion. The

results were similar to Al-Dujaily et al. (2013), in which the researchers demonstrated

that MBTI personality types Extroversion and Feeling were significant in determining

learner outcomes. However, the personality types influenced outcomes based upon

different factors, with Extroversion being related to procedural knowledge and Feeling

being related to declarative knowledge. The next step for discovering knowledge within

the asynchronous video e-learning environment is to determine which specific factors

within the learning environment influenced the interaction based upon the unique

personality traits.

Recommendations for future practice. The present study provided results that

can be applied to future practice. Specifically, these recommendations are applicable to

individuals interested in the consumer self-improvement e-learning market and

individuals concerned with the relationship between personality traits and learner

outcomes within video e-learning environments. Recommendations for future practice

are considerations to curriculum designers, independent e-learning creators, instructors,

and marketing managers for e-learning companies.

200

The first recommendation is for developers and producers of online video for

education to maintain a level of consistency in the presentation of e-learning. Trait

Openness, which describes an individual’s preference to try new things (McCrae &

Costa, 2003), was related to transactional distance within the video environment.

Individuals with low Openness prefer a consistent environment, while individuals high in

Openness seek to explore new areas of thought, art, and knowledge. To meet the needs

of both ends of the Openness spectrum, change should occur incrementally so as not to

disrupt the learning of low Openness learners while maintaining the engagement of high

Openness learners. Changes of which to be mindful include the video instructor’s

environment, graphics and additional video, the frequency, number, and types of learning

activities, as well as the use of multiple instructors. An individual sensitive to change

may interpret inconsistencies as a divergence to the dialogue, structure, or learner

autonomy elements of transactional distance, thereby widening the psychological and

communication connection between learner and learning environment.

A second recommendation is for course developers to include activities within

video-based curriculum to engage the learner. Activity is a foundation of constructivist

learning (Mason, 2013), and it is particularly necessary for individuals exhibiting higher

levels of Extroversion (McCrae & Costa, 2003). Integrating activities with video-based

e-learning is available on the consumer product level through a variety of e-learning

software packages, including the widespread e-learning development brands of Captivate

and Articulate. Activity in the form of quizzes, which were used in the present research,

puzzles, and online laboratories improve learner interaction and reinforce learning (Lucas

et al., 2013). However, the amount of activity should be commensurate with the learning

201

objectives and should not induce learner fatigue, particularly in a consumer self-

improvement course. Individuals that display Introversion may grow disinterested with

high levels of activity and abandon the course, as they perceive a growing demand on

their psychological resources to remain engaged (Al-Dujaily et al., 2013).

202

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Appendix A

IRB Approval Letter

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Appendix B

Informed Consent

Grand Canyon University

College of Doctoral Studies

3300 W. Camelback Road

Phoenix, AZ 85017 Phone: 602-639-7804

Email: [email protected]

CONSENT FORM

THE RELATIONSHIP BETWEEN PERSONALITY TRAITS EXTROVERSION AND AGREEABLENESS WITH TRANSACTIONAL DISTANCE WITHIN THE

ASYNCHRONOUS VIDEO E-LEARNING ENVIRONMENT

INTRODUCTION The purposes of this form are to provide you (as a prospective research study

participant) information that may affect your decision as to whether or not to participate in this research and to record the consent of those who agree to be involved in the study.

RESEARCH

Burton A. Casteel, III, Doctoral Student, Grand Canyon University, has invited your

participation in a research study.

STUDY PURPOSE

The purpose of the research is to see if a relationship exists between an individual’s personality traits and their level of interaction within an online instructional environment that features video instruction.

DESCRIPTION OF RESEARCH STUDY

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RISKS There are no known risks from taking part in this study, but in any research, there is

some possibility that you may be subject to risks that have not yet been identified.

BENEFITS Although there may be no direct benefits to you, main benefits of your participation in

the research are to assist in identifying characteristics of learners that can be matched with specific learning environments, such as video or text-based learning, for improved learning outcomes.

NEW INFORMATION

If the researchers find new information during the study that would reasonably change

your decision about participating, then they will provide this information to you.

CONFIDENTIALITY All information obtained in this study is strictly confidential. The results of this research

study may be used in reports, presentations, and publications, but the researchers will not identify you. In order to maintain confidentiality of your records, Burton A. Casteel, III, will maintain all data in an offline server, with no personally identifying information associated with the data other than the specific information collected. Your email address will not be stored, nor will it be associated with your responses. Your data will only be identifiable by a subject code, which is a number assigned to the data based upon the order in which you complete the survey (e.g., the first person to complete the survey is 001, the next is 002, and so on.) The only person with access to the data is the researcher.

WITHDRAWAL PRIVILEGE

Participation in this study is completely voluntary. It is ok for you to say no. Even if you

say yes now, you are free to say no later, and withdraw from the study at any time.

If you decide to participate, then as a study participant you will join a study involving

research of personality traits within the online learning environment. If you agree to participate, you will complete surveys that ask questions about you, including your age, gender, experience in online activities, relationship status. Also included will be items asking you to rate yourself as agreeing or disagreeing that certain phrases describe you. You will then participate in the online course covering the topic of communication skills for relationships. Upon completing the course is one more survey that asks questions about the course design (not the course content). Although answering all the questions will greatly assist in completing this study, you may skip questions if you choose. At the end of the survey, you will also be offered the opportunity to have someone follow up with you if you have any additional questions about this research.

If you say YES, then your participation will last for approximately 30 – 45 minutes in the online environment, so using a screen size that is comfortable for you over longer periods of time is advisable. Approximately 200 people will be participating in this study from across the United States.

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COSTS AND PAYMENTS

There is no payment for your participation in the study, although you will receive

access to the Communication Skills for Relationships course at no charge. VOLUNTARY CONSENT

Any questions you have concerning the research study or your participation in the

study, before or after your consent, will be answered by Burton A. Casteel, III, at (480) 694- 0662 or at [email protected].

If you have questions about your rights as a subject/participant in this research, or if

you feel you have been placed at risk, you can contact the Chair of the Institutional Review Board, through the College of Doctoral Studies at (602) 639-7804.

This form explains the nature, demands, benefits and any risk of the project. By

signing this form you agree knowingly to assume any risks involved. Remember, your participation is voluntary. You may choose not to participate or to withdraw your consent and discontinue participation at any time without penalty or loss of benefit. In signing this consent form, you are not waiving any legal claims, rights, or remedies. By signing this form, you are affirming that you are at least 18-years of age. A copy of this consent form will be given (offered) to you.

Your signature below indicates that you consent to participate in the above study. _______________________ ______________________ ____________ Subject's Signature Printed Name Date _______________________ ______________________ ____________ Other Signature Printed Name Date (if appropriate)

INVESTIGATOR’S STATEMENT "I certify that I have explained to the above individual the nature and purpose, the potential

benefits and possible risks associated with participation in this research study, have answered any questions that have been raised, and have witnessed the above signature. These elements of Informed Consent conform to the Assurance given by Grand Canyon University to the Office for Human Research Protections to protect the rights of human subjects. I have provided (offered) the subject/participant a copy of this signed consent document."

Signature of Investigator____________________________ Date___________

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Appendix C

Copy of Instruments and Permissions Letters to Use the Instruments

The Big Five Inventory (BFI)

Here are a number of characteristics that may or may not apply to you. For example, do you agree that you are someone who likes to spend time with others? Please write a number next to each statement to indicate the extent to which you agree or disagree with that statement.

Disagree strongly

Disagree a little

Neither agree nor disagree

Agree a little

Agree strongly

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I see Myself as Someone Who... ___1. Is talkative ___2. Tends to find fault with others ___3. Does a thorough job ___4. Is depressed, blue personality ___5. Is original, comes up with new ideas ___6. Is reserved ___7. Is helpful and unselfish with others ___8. Can be somewhat careless ___9. Is relaxed, handles stress well ___10. Is curious about many different things ___11. Is full of energy ___12. Starts quarrels with others ___13. Is a reliable worker ___14. Can be tense ___15. Is ingenious, a deep thinker ___16. Generates a lot of enthusiasm ___17. Has a forgiving nature ___18. Tends to be disorganized ___19. Worries a lot ___20. Has an active imagination ___21. Tends to be quiet ___22. Is generally trusting ___23. Tends to be lazy ___24. Is emotionally stable, not easily upset ___25. Is inventive ___26. Has an assertive ___27. Can be cold and aloof ___28. Perseveres until the task is finished ___29. Can be moody ___30. Values artistic, aesthetic experiences ___31. Is sometimes shy, inhibited ___32. Is considerate and kind to almost

everyone ___33. Does things efficiently ___34. Remains calm in tense situations ___35. Prefers work that is routine ___36. Is outgoing, sociable ___37. Is sometimes rude to others ___38. Makes plans and follows through with them ___39. Gets nervous easily ___40. Likes to reflect, play with ideas ___41. Has few artistic interests ___42. Likes to cooperate with others ___43. Is easily distracted ___44. Is sophisticated in art, music, or literature

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Please check: Did you write a number in front of each statement?

BFI scale scoring (“R” denotes reverse-scored items):

Extraversion: 1, 6R, 11, 16, 21R, 26, 31R, 36

Agreeableness: 2R, 7, 12R, 17, 22, 27R, 32, 37R, 42

Conscientiousness: 3, 8R, 13, 18R, 23R, 28, 33, 38, 43R

Neuroticism: 4, 9R, 14, 19, 24R, 29, 34R, 39

Openness: 5, 10, 15, 20, 25, 30, 35R, 40, 41R, 44

Permission for BFI use granted to all non-commercial researchers per Berkeley Personality Lab website (John, 2009).

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Structure Component Evaluation Tool

Course Title:

___________________________________________________________________

Rater:

________________________________________________________________________

Rate each item as to the degree which the elements are present in the online course.

0 – not evident 1 – minimally evident 2 – moderately evident 3 – fully evident

Listing of Descriptors Descriptor Rating

Content Organziation

Overall

Media such as graphics, animations, diagrams, video, and audio that are utilized are relevant to the course.

Objectives match the course exams. Glossary or additional references are provided. Each course unit/module contains clear objectives of the material to be presented.

Course objectives are present. Course provides FAQ’s or equivalent. Content/instruction contained in course is appropriate for the target audience.

Syllabus

Instructor grading policies are present. Participation requirements are provided. Contains information regarding course policies (i.e., late assignments, make-up policies, etc.)

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Technical support contact information is provided. Point value of all assignments is available Information regarding student support services is available in the course.

Faculty contact information is present. Instructor provides guidelines for all student communication.

Course provides detailed directions on how to submit each assignment or activity.

Information about any prerequisites or entry-level skills needed is present

Instructor provides expectations regarding discussion posts or other class interactions (synchronous or asynchronous.)

Guidelines were provided regarding all offline student communication (i.e. posting transcripts of offline meetings for a group.)

Course description is present. Each course unit/module contains a clear overview of the material to be presented.

Course Schedule

Course contains due dates for assignments Course contains assignments by week (or other time unit, including calendar dates.)

All exam or assessment dates are provided. Suggested begin dates for each unit/module are provided.

Contains a course calendar that includes important course dates.

Delivery Organization

Overall Course provides a layout screen (homepage) that is clear, clean, and well organized.

237

Course provides on screen instructions that are simple, clear, and concise of how to begin.

Student has the ability to bookmark areas of the course.

Course provides clear exit/logoff paths.

Consistency Course has a menu that remains constant as the student moves within the course.

Course provides on screen navigation (i.e. breadcrumbs) to let the learner know where they are in the course.

Each module/unit is accessed in the same manner throughout the course.

Course has a menu that remains constant as the student moves within the course.

Each course unit/module contains a single page that communicates all activities to be completed.

Course unit/modules are presented consistently throughout the course.

Flexibility

All assignments including assigned reading is available for access.

Ability to access archived discussions (i.e. synchronous chats or desktop conference meetings) are provided.

Students can proceed at their own pace. The course contains flexible or adaptable learning routes.

Students can review previous frames of information unlimited times.

Student can pause or replay any audio or video segment as desired.

Previously viewed on screen instructions can be skipped.

Learner has control over the rate of presentation of

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material. Course Interactions Organization

Student-to-Student Student-to-student communication behaviors are clearly communicated.

Student-to-student communication methods were clearly communicated.

Student-to-Instructor

Faculty provides information as to their timeliness of responses to email and student inquiries.

Instructor is available for phone or face-to-face conferencing.

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Figure C1. SCET permission letter. Communication from Cheryl Sandoe providing

permission to use the Structure Component Evaluation Tool as communicated via

LinkedIn social network.

240

Pre-course Survey: Pre-course survey Please answer all of the following questions. Do not spend too much time on any one question. Your first thought is usually your most accurate one. A little bit about you What is your age? ¢ Under 20 years ¢ 20 - 29 years ¢ 30 - 39 years ¢ 40 - 49 years ¢ 50 - 59 years ¢ 60 - 69 years ¢ 70 years or older What is your gender? ¢ Male ¢ Female Which of the following best describes your relationship status? ¢ Single ¢ Dating ¢ Engaged ¢ Married ¢ Divorced ¢ Separated ¢ Widow/Widower ¢ Other A little bit about your technology How many hours each week do you spend on a computer or on the Internet? ¢ Less than one hour ¢ 1 - 3 hours ¢ 3 - 6 hours ¢ 6 - 9 hours ¢ 9 or more hours What Internet connection type are you using right now? ¢ High Speed ¢ Mobile Network ¢ Dial Up ¢ Other What type of device are you using right now? ¢ Desktop ¢ Laptop ¢ Tablet ¢ Phone ¢ Other

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Appendix D

Recruitment Script

Grand Canyon University

College of Doctoral Studies

3300 W. Camelback Road

Phoenix, AZ 85017

Phone: 602-639-7804

Email: [email protected]

RECRUITMENT SCRIPT

The following is the script for the pre-recorded video message:

Thank you for participating in this educational study. Your role in this research

can be broken down into three phases. In the first phase, you will be presented with a

form describing the research, after which you will complete a short questionnaire. In the

second phase, you will take a short class about some specific communication skills that

you can apply to your relationship. Following the course segments, which last about 15

minutes total, you’ll be asked a few questions about the course. Again, we appreciate

your time and the valuable contribution you are making towards learning research. To

get started, please press the NEXT button below.

The next page describes the nature of the research study, which is to examine any

relationships between personality traits and interaction levels with an online video course.

There are no known risks for participating in this study and the benefits may include

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furthering scientific knowledge about personality traits and learning, and you will be

given the opportunity to learn communication skills that may be valuable in your

relationship. Of course, all the information you provide is strictly confidential and your

name will never be released for any purpose. Your participation in this study is

completely voluntary and you may withdraw from the study at any time without any

negative consequences. If you have any questions about the study, you are welcome to

contact the researcher. Please press the NEXT button below to review the form and then

type your name in the signature box to indicate your consent to participate.

The following script is the information presented on screen during the recruitment

script narration:

Burton A. Casteel, III, under the direction of Dr. Audrey Rabas in the College of

Doctoral Studies at Grand Canyon University, is conducting research for the purpose of

investigating the potential correlation between personality traits and perceived learner

interaction within a video learning environment. Mr. Casteel is recruiting individuals to

respond to questions about themselves, such as age, gender, and computer and Internet

usage; to take a personality test; to complete a 15-minute course on communication

skills; and to answer questions about your perceptions of the course. The entire process

should take between 30 and 35 minutes. You must be at least 18 years of age to

participate.

Your participation is in this study is completely voluntary. If you have any

questions concerning the research study, please contact Mr. Casteel at (480) 694-0662 or

at [email protected].

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Appendix E

Recruitment Materials

Figure E1. Ad #1 of Google AdWords campaign.

Figure E2. Ad #2 of Google AdWords campaign.

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Figure E3. Ad #3 of Google AdWords campaign.

Figure E4. Ad for paid Facebook social media recruitment campaign.

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Figure E5. Front side of recruitment postcard.

Figure E6. Back side of recruitment postcard.

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Appendix F

Tables and Charts for Statistical Analyses

Figure F1. Histogram of trait Openness within sample population. M = 63.31, SD =

14.04, N = 98.

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Figure F2. Box chart for trait Openness from sample population.

Figure F3. Normal Q-Q plot of trait Openness from sample population.

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Figure F4. Histogram of trait Conscientiousness within sample population. M = 72.04,

SD = 13.11, N = 98.

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Figure F5. Box plot of trait Conscientiousness from sample population.

Figure F6. Normal Q-Q plot of trait Conscientiousness from sample population.

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Figure F7. Histogram of trait Extroversion within sample population. M = 57.70, SD =

19.93, N = 98.

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Figure F8. Box plot of trait Extroversion from sample population.

Figure F9. Normal Q-Q plot of trait Extroversion from sample population.

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Figure F10. Histogram of trait Agreeableness within sample population. M = 74.89, SD

= 10.92, N = 98.

Figure F11. Box plot of trait Agreeableness from sample population.

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Figure F12. Normal Q-Q plot for trait Agreeableness from sample population.

Figure F13. Histogram for trait Neuroticism within sample population. M = 36.70, SD =

15.32, N = 98.

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Figure F14. Box plot for trait Neuroticism from sample population.

Figure F15. Normal Q-Q plot for trait Neuroticism from sample population.

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Figure F16. Histogram for SCET values within sample population with observed right

skewness (1.02). M = 11.32, SD = 5.27, N = 98.

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Figure F17. Box plot for SCET values from sample population.

Figure F18. Normal Q-Q plot for SCET values from sample population with

nonparametric values.

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Figure F19. Linearity test using scatterplot for personality traits and SCET values.

Generally ovular shapes indicate sufficient linearity between personality traits and SCET

to satisfy assumptions for correlation analyses and analyses of regression.

Table F1 Tests of Normality for Participant Personality Traits and TD Measures

Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig.

Openness .066 98 .200 .981 98 .181 Conscientiousness .101 98 .015 .971 98 .029 Extroversion .067 98 .200 .984 98 .294 Agreeableness .086 98 .073 .981 98 .156 Neuroticism .088 98 .059 .978 98 .104 SCET .158 98 .000* .887 98 .000* a. Lilliefors Significance Correction.

* p < .001.

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Table F2 Test of Homogeneity of Variances

Levene Statistic df1 df2 Sig. Openness .454 1 96 .502 Conscientiousness .082 1 96 .775 Extroversion 3.063 1 96 .083 Agreeableness .454 1 96 .502 Neuroticism .025 1 96 .876 SCET 1.260 1 96 .264 Note. No statistically significant results; therefore, the null hypothesis of homogeneity of variances is

accepted.

Table F3 Personality Trait Collinearity Statistics

Collinearity Statistics Trait Tolerance VIF Extroversion .97 1.04 Openness .93 1.08 Agreeableness .97 1.03 Conscientiousness .96 1.05 Neuroticism .90 1.11 Note. VIF < 10 in all cases, describing no collinearity between variables.

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Appendix G

Statistical Analyses

Table G1

Group Statistics of Internet Experience with SCET Values Usage N Mean Std. Deviation Std. Error Mean SCET <6 hours 22 11.76 5.85 1.25

6+ hours 76 11.19 5.12 .59 Table G2 Group Statistics for Gender with SCET Values

Gender N Mean Std. Deviation Std. Error Mean SCET Male 44 11.87 5.53 .83

Female 54 10.87 5.05 .69 Table G3 Descriptive Statistics of Device Type with SCET Values

N Mean Std.

Deviation Std.

Error

95 Confidence Interval for Mean

Minimum

Lower Bound

Upper Bound Maximum

Desktop 25 11.30 5.48 1.10 9.04 13.47 4.29 24.00 Laptop 48 10.19 4.04 0.48 9.02 11.36 5.20 24.00 Tablet 9 15.36 6.94 2.31 10.02 20.70 5.89 24.00 Phone 16 12.44 6.28 1.57 9.09 15.78 4.25 23.74 Total 98 11.32 5.27 0.53 10.26 12.37 4.25 24.00 Note. Dependent Variable: SCET.