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International Journal

Self

Directed Learning

Volume 7, Number 2

Fall 2010

The

International Journal of Self

Directed Learning

(ISSN 1934

3701) is published

biannually by the International Society

for Self

Directed Learning.

is a refereed,

electronic journal founded to disseminate scholarly papers that document research, theory,

or innovative or exemplary practice in self

directed learning.

Submission guidelines can

be found at

http://www.sdlglobal.com

SUBSCRIPTION or BACK COPY ORDERS: Contact:

International Journal of Self

Directed Learning

7339 Reserve Creek Drive

Port Saint Lucie, FL 34986

[email protected]

, International Society for

Self

Directed Learning

No portion of

this journal may be reproduced without written consent

Exceptions are limited to copying

as permitted by Sections 107 (“fair use”) and 108 (“libraries and archives”) of the U. S.

o obtain permission for article duplication, contact the editors at:

International Journal of Self

Directed Learning

7339 Reserve Creek Drive

Port Saint Lucie, FL 34986

[email protected]

Cover design by Gabrielle Consulting

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

International Journal of Self

Directed Learning

Volume 7

Number

Fall 2010

EDITOR

Lucy Madsen Guglielmino,

Florida Atlantic University

EDITOR EMERITUS

Huey B. Long,

University of Oklahoma

(Emeritus)

EDITORIAL BOARD

Naomi Boyer,

University of South Florida Polytechnic

Ralph G. Brockett,

University of Tennessee

Robert J. Bulik,

University of Texas Medical

Branch

Rosemary Caffarel

la,

, Cornell University

Philippe Carré,

Université Paris Ouest Nanterre La Défense

, France

Gary J. Confessore

George Washington University

(Emeritus)

Richard

Durr,

Online Training Institute

Brian Findley,

Palm Beach State

College

Paul J. Guglielmino,

Florida Atlantic University

(Retired)

Joan H. Hanor

, California State University San Marcos

Roger Hiemstra,

Syracuse University

(Emeritus)

Waynne James,

University of South Florida

Carol Kasworm,

Nor

th Carolina State University

William J. Kops,

University of Manitoba, Canada

Theresa N. Liddell,

School District of P

alm Beach County

Patricia A. Maher,

University of South Florida

Sharan Merriam,

University of Georgia (Emeritus)

Magdalena Mo Ching Mok,

The Hong Kong Institute of Education

Albertina Olivei

, University of Coimbra, Portugal

EunMi Park,

Johns Hopkins University School of Medicine

Janet Piskurich,

Stephen S. Foster Medical School, Texas Tech

George Piskurich

ACS, a Xerox Company

Michael K. Ponton,

Regent University

Kathleen B. Rager,

University

of Oklahoma

Thomas G. Rei

o, Jr.,

Florida International University

Karen Wilson Scott,

Idaho State University

Peter L. Zsiga,

Indian River State College

Editorial Associate: Elizabeth G.

Swan

Webmaster

Richard E. Durr,

Online Training Institute

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Preface

This issue touches on several important issues and trends in the field of self

directed learning today: the internet and SDL, instrumentation, further exploration of

how leaders use

self

directed learning

in their leadership role

s, and ways of

incorporating self

directed learning

into formal instructional settings.

As internet use increases exponentially, options for self

directed learning

become more prevalent, more accessible, and m

ore varied. In the first article in this

issue, Kop and Fournier explore the options and actions of self

directed learners in the

connectivist environment of a Massive Open Online Course (MOOC). They describe

new dimensions

of self

directed learning

that

emerged in their research, which was

conducted under the auspices of the National Research Council of Canada.

Moving from the frontier of internet learning environments to the more

familiar setting of organizations devoted to community service, Phares and

Guglielmino report on an examination of the self

directed learning readiness of

community leaders, describe the types of learning projects that contribute to the

performance of their leadership roles, and document their belief that ongoing self

directed l

earning is essential if they are to fulfill their responsibilities well.

Two articles in this i

ssue address instrumentation. Ki

rwan, Lounsbury, and

Gibson

explore the relationship of self

direction in learning

and the Big Five and

narrow personality traits in the Resource Associates Transition to College inventory

(RATTC). Ponton, Carr, Schuette, and Confessore present an analysis of the

usefulness of the

Appraisal of Learner Autonomy (ALA), a measure of self

efficacy in

autonomous learning, as a part of the Learner Autonomy Profile (LAP).

Finally, in a practice brief MacDonald and McLaughlin,

two gifted professors

describe

creative

ways of incorporating self

directed learning

into English classes in a

college

setting, focusing on creating integrated projects that require independent

thought and effort as alternatives to traditional

approaches to instruction and

assessment.

Lucy Madsen Guglielmino, Editor

Huey B. Long, Editor Emeritus

A special note: The

JSDL

will now be accepting articles written only using the 6

edition of the American Psychological Association’s Publication Manual.

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

iii

________________________________________

______________________________

International Journal of Self

Directed Learning

Volume 7, Number 2

, Spring 2010

CONTENTS

Preface

New Dimensions of Self

Directed Learning in an Open

Networked Learning

Environment

Rita Kop and

Hélène

Fournier

The Big Five and Narrow Personality Traits

in Relation to Self

Direction in Learning

Jeral R. Kirwan, John W. Lounsbury, and Lucy W. Gibson

The Role of Self

Directed Learning in the Work of Community Leaders

Leatrice T. Phares

and Lucy M. Guglielmino

Research Brief

Self

Efficacy and the Learner Autonomy Profile

Michael K. Ponton, Paul B. Carr, Christine T. Schuette, and Gary J.

Confessore

Practice Brief

Fostering Self

Directed Learning in an

Honors Classroom Through Unconventional

Methods And Assessment

Nancy D. McD

onald a

nd Idell McL

aughlin

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

NEW

DIMENSIONS TO SELF

DIRECTED LEARNING

IN AN OPEN NETWORKED LEARNING ENVIRONMENT

Rita Kop and

Hélène

Fournier

Abstract

New technologies have changed the educational landscape. It is now possible

for self

directed learners to participate informally in learning events on open online

networks, such as in Massive Open Online Courses. Our research analyzed the agency

and level

of autonomy required by learners participating in a course of this nature.

Using Bouchard’s four

dimensional model of learner control, we found that there are

new dimensions to self

directed learning in conne

ctivist learning environments.

The

research al

so brought to light new challenges and opportunities for self

directed

learners who might not be able to call on trusted educators for support in their learning

endeavors, but rely on the aggregation of information and informal communication

and collaborat

ion available through social media to advance their learning.

The proliferation of Information and Communications Technology (ICT) in

recent years has changed the educational landscape. It has added to the complexity of

our lives and aided in the

creation of a plethora of new opportunities for learning.

Faculty members are changing their practice and are experimenting with open

educational resources and cloud computing, such as Massive Open Online Courses

(MOOC), acknowledging that informal and se

directed learning now form part of

our everyday existence. The technology, however, raises new challenges and

opportunities for the self

directed learner, who might no longer be able to call on a

trusted educator for support in his or her learning ende

avor.

The emerging technologies that are currently shaping the Internet and the Web

provide us with access to information and the ability to work and learn with others in a

creative global collaboration outside the educational structures that have been the

norm for centuries (Downes, 2010; Fournier & Kop, 2010). New structures and

environments are in place where people can learn autonomously, but one might

question if people will be able to do so effectively (Kop & Bouchard, 2011). Two

areas of research ar

e foundational to examining learning in open networked

environments: learner autonomy and connectivism.

Research conducted under the auspices of the National Research Council of Canada

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Learner Autonomy

Several researchers in the field of self

directed l

earning see learner autonomy

as an important component of self

directed learning (Ponton, 2005; Bouchard, 2009;

Boucouvalas, 2009). Bouchard (2009) and Boucouvalas (2009) both highlighted the

learning environment, learning context, and the connections peop

le make during their

earning as determining factors

in the success of self

directed learning journeys. These

elements are aligned with Bandura’s (2002) ideas on “human agency” (p. 269). He

accentuated three types of agency:

personal agency

exercised indi

vidually,

proxy

agency,

in which people secure desired outcomes by influencing others to act on their

behalf; and

collective agency,

in which people act in concert to shape their future in

whatever cultural

context they inhabit

Bandura emphasized the impo

rtance of all

three agencies and their interrelatedness in the complex world in which we now live.

Tough (1979) and Grow (1991) noted that learners move through different

phases of self

direction, and Bouchard (2009) identified particular factors that

inf

luence autonomous learning strategies. He clustered them in four dimensions, one

dealing with psychological issues, one with pedagogical issues, and two with

environmental issues:

The first dimension, which he called the

conative

one, relates to psycholog

ical

issues such as drive, motivation, initiative and confidence. In this dimension

Bouchard also highlighted aspects of context and transitions, how these

influence people’s urges to take up learning, and the social networks that

people are involved in a

nd which act as affective support and resources. He

noted that their past learning experiences might also influence autonomous

learning strategies.

The

algorithmic

dimension relates to pedagogical issues, for instance the

sequencing, pacing and goal sett

ing in learning, the evaluation of progress, and

final evaluation and preparation for validation. These are clearly tasks that in

the past were carried out by the educator; in an autonomous learning

environment, they become issues that learners themselves

have to resolve.

Bouchard (2009) also saw two environmental clusters of factors that would

influence learning strategies:

The dimension that Bouchard called the

semiotics

of learning is related to the

delivery model of resources. This model has drastical

ly changed in recent

years and moved from the use of resources such as books and paper to

electronic texts and multimedia, which might be stored in searchable databases

that could be linked through hyperlinks. It could also include contributions in

blogs,

wikis, and synchronous and asynchronous communication. Information

is obtained through social networks and learners will need to be able to

evaluate and navigate this new information landscape.

The importance of aspects of e

conomy

was recognized as a fourth category: the

perceived and actual value of the learning, the choice to learn for personal gain

such as for future employment, and the possible cost of other study options.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

While Bouchard’s dimensions provide an important basis

for exploration of learner

autonomy, examining self

directed learning in an open networked learning

environment also requires awareness of the challenges of

connectivism

Connectivist Learning in an Online Environment

A current example of self

directed learning promoted by Downes (2010) and

Siemens (2008) is based on

connectivism

. They posit that being a member of an

online network, communicating with others and filtering information and ideas that

others provide will

lead to knowledge creation and learning advancement.

Connectivism advocates the active engagement of people with resources in

communication with others, rather than the transfer of knowledge from educator to

learner. Moreover, they promote a learning org

anization whereby there is not a body

of knowledge to be transferred from educator to learner, and where learning does not

take place

in a single environment. Instead, knowledge is distributed across the Web

and people’s engagement with it constitutes lear

ning. This

model recognizes that the

increasing influence of the Web and the global online connectedness of people will

have implications for people’s learning (Siemens, 2008; Fournier & Kop, 2010). The

role of the educator is predicted to change (Downe

s, 2010) as learners have the option

to move from a learning environment controlled by the educator and the institution to

an environment where they find their own information and direct their own learning as

they develop ideas and connect with (knowledgea

ble) others on networks away from

the formal setting.

A connectivist approach and learning environment might pose new challenges

for learners who direct their own learning; but it is likely that such an approach will

also provide new opportunities to enhan

ce their learning experiences. The current

literature related to Web development highlights four challenges and pertinent

developments to connectivist learning:

The

nature of the network

as a place to learn as opposed to a group in an

educational institu

tion and the levels of

presence

in each has been highlighted

as an important factor in the willingness of participants to actively engage

online (Dron & Anderson, 2007). Power relations in online networks and how

these might influence the information and r

esources that self

directed learners

will be able to access are other important issues. The structures of the Web are

preventing it from developing into a network where equality is the norm, rather

than the exception (Baraba

si, 2003; Boyd, 2010b

). In add

ition, the increasing

influence of commerce on the Web might negatively influence the potential of

the social Web for learning and education (Friesen, 2010, Lanier, 2010)

Some

literacies

have been identified that are critical for learners to be able to

eff

ectively direct their own learning in an open online networked environment.

Apart from reading and writing, these include information and media literacy

and the ability to critically analyze resources and information in order to

understand the new semantic

s of the Web. Creative abilities and a flexible

mindset in an environment that is characterized by change and complexity

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

have also been highlighted as important (Downes, 2009; Partnership 21

Century Skills, 2009; Sahlberg, 2009).

Cloud computing

and the

emergence of Web2.0 and social media have altered

the dynamics of the Web. Moving away from a linear process of printed text,

they increasingly involve the production of digital artefacts and the storage of

these away from local computing devices, as well

as the use of a variety of

communication, collaboration and sharing tools that people find and use on the

Web. These tools have created a new demand on human agency in the form of

creativity, innovation and self

expression

(Sahlberg, 2009

; Fisher,

Giaccar

di,

Eden, Sugimoto,

& Ye,

2005).

The

Semantic Web and learning analytics

are the latest developments of the

Web and can be used for the visualization of large amounts of data, creating a

need for learners to be able to understand and critically analyze gra

phs and

figures. The analysis of this “Big Data” can also be used to improve learning in

new ways, and some observers envisage the use of analytics in learning

recommender systems to aid learners in their information aggregation

strategies (Rogers, McEwen

, & Pond, 2010; Fournier, Kop

& Sitlia, 2011).

In order to develop empowering learning environments that foster active

learning, designers and developers of such environments first need to understand the

factors that influence people’s attitudes, intenti

ons and behaviours. They must also

understand the prerequisites for people to thrive in such environments in order to

create favourable components and conditions. This paper will investigate whether the

four dimensions that Bouchard (2009) highlighted in

his research match the

experiences and perceptions of learners in a Massive Open Online Course that was

held in the autumn of 2010 and if additional dimensions might be justified by

examining their connectivist learning in an online environment.

The Res

earch on Self

Directed Learning in a Massive Open Online Course

(MOOC)

Recognizing the challenges posed by innovations in Web

based learning,

learning technologists have started developing structures to support autonomous

learners in the negotiation of

this new and ever

changing learning landscape. Carroll,

Kop, and Woodward (2008) see the creation of a place where people feel comfortable,

trusted, and valued as the crux to engaging learners in an online environment. The

task would be to move towards a

space that aggregates content and imagine it as a

community, a place where dialogue happens, where people feel comfortable, and

interactions and content can be accessed and engaged with easily: a place where the

personal meets the social with the specific

purpose of the development of ideas and of

learning.

The National Research Council of Canada is in the process of designing and

developing a place that might support autonomous learners online. It is a Personal

Learning Environment (PLE) called Plearn

. The development consists of two strands:

The creation of a place, encompassing technological components, where people can

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

manage their own learning, and the creation of a pedagogical platform that would

support learners in this endeavor. The research to

achieve the design and development

of such a PLE consisted of several strands, but this paper

will report only on some

components of the educational research: issues relevant to self

directed learning on a

MOOC.

The Setting

The Massive Open Online Course

(MOOC) researched was organized by the

National Research Council of Canada as part of their research in Personal Learning

Environments in cooperation with Athabasca University and the University of Prince

Edwards Island. The subject under scrutiny was Per

sonal Learning Environments,

Networks and Knowledge (PL

ENK). It was a free course that

lasted 10 weeks with a

total of 1641 participants registered. PLENK2010 did not consist of a body of content

and was

not conducted in a single place or environment. It w

as distributed across the

Web.

Two of the facilitators of the course were the founders of

connectivism

approach to learning that has been earmarked by some as the learning theory for the

century

Siemens

and Downes (

2009) have highlighted on numerous occasions

the importance of human agency and the necessity of active participation in

connectivist learning

They stress the importance of four types of activity for

successful learning:

(a) aggregat

ion of information, (b) remixing and reflecting on the

resources and relating them to what people already know, (c) repurposing:

creating

something of their own, and

) sharing their work and activities with others.

The central resource in the course was

The Daily

, a newsletter that participants

could subscribe to if they wished, which displayed the aggregated resources and

artifacts produced by participants in the course. In addition, the Moodle Learning

Management System with wiki was used to hold discussions an

d to display course

resources and the schedule for speakers of twice weekly Elluminate sessions.

Throughout the course Twitter and participants’ and facilitators’ blogs developed

around the course subject, and Facebook Groups, Second Life, and other social

network environments were developed by participants.

Learner support was provided by four facilitators in the form of videos,

slideshows, and discussion posts in addition to blog posts, feedback to blogs, and

Moodle discussion posts. Once a week Ellumina

te was used by facilitators for a

synchronous discussion and chat session on that week’s subject.

Research Methodology

Research in the intricacies of learning taking place on online networks is one

of the axes of the research into the design and developme

nt of a PLE. If people are

encouraged to move away from the institution for their learning, it is important to find

out the relevance to the learning experience of the informal (online) networks in which

they find their information and where they might dev

elop. A network in the context of

this paper would be an open online space where people meet, as nodes on networks,

while communicating with others and while using blogs, wikis, audio

visuals, and

other information streams and resources. De Laat (2006) hig

hlighted the complexity of

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

researching networked learning and emphasized as key problems the issues of human

agency and the multitude of issues involved, such as the dynamics of the network,

power

relations on the network, and the amount of content generat

ed. Effective

analysis would require a multi

method approach and would involve new ethics and

privacy issues.

New ethics and privacy issues in networked environments.

Every

researcher has to consider the ethical implications of the chosen methods of obtai

ning

the data for a study and the use made of it. Sometimes obtaining data is a matter of

accessing statistics or documents. When human subjects are involved in the research,

careful consideration of the level of informed consent by participants is also

required.

Miller and Bell (2002) argued that gaining informed consent is problematic if it is not

clear what the participant is consenting to and where “participation begins and ends”

(p. 53). Several ethical issues were raised in the literature, of which

misuse of data and

privacy issues were the most important. Van Wel and Royakkers (200

4) and Boyd

(2010a

) caution that data could pose a threat to subjects when misused, or used for

different purposes than what it was supplied for. Researchers should at l

east

anonymise data in order to respect privacy issues (Van Wel & Royakkers, 2004;

Rog

ers et al., 2010; Boyd, 2010a

). It has also been suggested by network researchers

that people should have the choice to opt in or opt out of the use of their data. If

som

eone is not aware that the data is being collected or how it will be used, he/she has

no real opportunity to consent or withhold consent for its collection and use. This

invisible

data gathering is common on the Web (V

an Wel & Royakkers, 2004

) and

highligh

ts some new decisions related to ethics that researchers will have to make. We

feel that researchers have a responsibility to carefully consider the context of their

research, and also the process that takes place between observing, collecting and

analyzin

g “Big Data”; data that is left by traces of activities that might not at all be

related to the visible participation of learners.

In this study “Big Data” was captured out on open networks. The research

team set out the boundaries of the research on the

consent form that participants were

asked to read at the start of the course. They were informed that data collection would

include learning

related activities in the course environment and also learning

activities that happened outside the course, but wh

ere the course tag #PLENK2010

was being used.

Data on PLENK2010 was collected according to these principles: using

quantitative as well as qualitative measures, asking for informed consent, and using

the #PLENK2010 tag to identify course

related data outsi

de the course environment

that learners would consent to include in the research.

Quantitative data collection

. Three surveys were carried out near the end of

the course and after it had finished in order to capture and explore learning

experiences during

the course: including the End Survey (N

63); an Active

Producers Survey (N

= 32), that was filled out by people after an invitation was posted

in the course blog for people who had produced

more than two digital artifacts

; and a

Lurkers Survey (N

74) t

hat was filled out after a similar call for people who had

limited their participation in the course to producing less that 2 digital artifacts and

whose behavior was characterized in a consuming rather than a participating nature.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

The Moodle data mining f

unctionality was used to gather participant details, their

level of use and access of resources, information on course activities, and discussions

taking place in the course forums.

Qualitative data collection

. In addition, qualitative methods in the form

virtual ethnography were used. An ethnographer was working on the course, collecting

qualitative data through observation of activities and engagement. She also

interviewed and surveyed a number of participants during the final week and held a

focus g

roup with ‘silent participants’ (lurkers) after the course to gain a deeper

understanding of particular issues related to the active participation of learners. The

researchers were interested in the processes taking place and the perspectives and

understan

dings of the people in the setting; what Hammersley

et al. (2001) describe

the “details, context, emotion and the webs of social relationships that join persons to

one another” (p. 55). Hine (2005) highlighted that on the Web the technology itself

and

the artifacts it produces should be taken into consideration in the online

ethnography, as these are part of the research setting and might influence the human

interactions researched. As vast amounts of discursive data were generated in this

form of netw

orked learning in an open environment, computational tools such as

Nvivo were used for analyses and interpretation of the qualitative research data. It was

fairly easy to capture vast amounts of qualitative data through the aggregation tools

such as the gR

SShopper aggregator that was being used to feed into the newsletter

(The Daily).

Data analysis

. Learning analytics tools were used as a form of Social Network

Analysis (SNA) to clarify activities and relationships between nodes on the PLENK

network. SNA

also provided information on the importance of “connectors” on other

networks, and the most relevant tools to facilitate this. Secondary data analysis was

carried out on the Moodle logs. The gRSShopper aggregator statistics functionality

provided details

on course

related use of blogs and micro

blogging tools such as

Twitter. Some analytics and visualization tools, such as the Social Networks Adapting

Pedagogical Practice (SNAPP) tool, were also used to deliver real

time social network

visualizations of Mo

odle discussion forum activity; while the visualization tool

NetDraw was used to create an ego network for understanding the role of a particular

actor in a discussion.

Because of the volume of data generated by the 1641 participants and

facilitators, qua

ntitative analysis of blog posts and Twitter a

nd Moodle participation

was

used, but the analysis of quali

tative data was

restricted to the Moodle environment

and some blogs that were representative of all the blog posts produced by participants.

Findings

Participants

’ Ages and Locations

The professional background of participants on PLENK was mainly related to

education, research, and design and development of learning opportunities and

environments. Participants were teachers, researchers, managers, mentors, engineers,

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

facilitators, trai

ners, and university professors. Figure 1 sh

ows PLENK participants’

age, with a majority of participants in the course over 55 years old.

Figure 2 shows a Google Map, instigated by one of

the PLENK participants,

representing participants’ residence.

high number were from the USA, Canada, and

Europe, although participants were from a total of 69 countries.

Participation Levels

When the course started, 846 had re

gistered; participation increased to 1641

the end of the course, as shown in Figure 3. Twice

weekly meeting sessions were

hosted on Elluminate; once a week with an invited speaker and once as a

discussion

session amongst the group and facilitator(s). Actual presence at these synchronous

Figure

PLENK participants' age

Figure

PLENK participants' locations

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

sessions decreased over the weeks from 97 people in week two, when attendance was

the highest, to 40 in the final week with a similar trend in accessing recordin

gs for the

sessions.

Global participation and multiple time zones influenced who could be

present and who accessed the recordings. A high number of blog posts were generated

related to the course (900) and an even higher number of Twitter contributions (

3104).

The #PLENK2010 identifier facilitated the easy aggregation of blog posts, social

bookmarking links, such as delicious, and Twitter messages produced by participants,

which highlighted a wide number of resources and links back to participant’s blogs

and discussion forums; thus connecting different areas of the course.

Although the

number of course registrations was high, an examination of contributions across weeks

(i.e., Moodle discussions, blogs, Twitter posts marked with #PLENK2010 course tag,

and

participation in live Elluminate sessions) suggested that about 40

60 individuals

on average contributed actively to the course on a regular basis by producing blog

posts and discussion posts, while the remaining participants’ visible participation rate

as much lower.

Figure 4 shows the number of times people used particular tools, but

does not show how these interactions took place.

Some additional visualizations provided us with some more revealing pictures i

forum discussions and participation while using online tools. We have been

experimenting with several analytics tools, such as the social network analysis tool

SNAPP (Social

Network Adapting Pedagogical Practice) used a

s a bookmarklet to the

browser.

The activation of the SNAPP tool resulted in network visualizations and the

data generated was also exported to both VNA (Edgelist format) and GraphML

formats. The creation of the

network visualizations clarified the role that an actor

might play in a particular discussion

(Figures 5 &

Figure 3.

Plenk participation rates

Figure 4.

onnections between

participants

in a discussion

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Agency

and Active P

articipation

Some people with experience in learning in a MOOC were very involved in the

course

. One participant

produced a Google Map (see Figure 1) that

has received 22307

views

and a

blog

that has been read in 69 countries. The technical tools motivated

everal people to produce course

related artifacts. Some examples: one learner

produced a c

reative concept map (

Figure 7

)

. Another used Word

les

to ‘skim

read’

papers and

develop a visual impression of the content of a paper as shown in Figure 8.

Figure 7.

Example of learner concept map

(http://bit.ly/

hRBMSR)

Figure 8.

Wordle of paper by Drexler on the

networked student (http://bit.ly/g14Gov).

Figure 5.

Relationship between topics in a

discussion in week 1

Figure 6.

Learners as nodes on Twitter

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Not all participants contributed in a visibly active way. Many participants

accessed resources, but were not engaged in producing blog posts, videos, or other

digital artifacts.

The basis of MOOCs has always been four activities:

Actively

aggregating.

Actively relating these aggregated resources to earlier experiences and

knowledge, what Downes (2009) calls remixing.

Actively repurposing; producing a digital artifact with this mix of thoughts.

Actively sharing.

It was clear that in this course only a small percentage of participants engaged

in the production of digital artifacts. Between 40 and 60 were active producers; the

other 1580 were not active in this way. This outcome was unexpected to the course

organize

rs as they saw the production phase as vital to the learning in a networked

environment. As some participants mentioned in the discussion, if nobody is an active

producer, it limits the resources that all participants can use to develop their ideas,

discu

ssion, thinking, inspiration and learning.

The research data showed some

interesting reasons why the majority of participants were lurkers, rather than active

producers. As Figure 9 shows, 54.5% of respondents to the lurkers survey indicated

that they hav

e always been self

directed learners and do not think they have to actively

share and reply to discussion forums and blogs to learn. In addition, 50.9% highlighted

that

they are tactical lurkers who use particular strategies that are especially useful in

heir learning.

Figure 10 indicates that the most important restricting factors to participation

in PLENK were issues outside the course, related to people’s everyday lives, such as

time, job, family, and o

ther commitments, for 80.6% of respondents to the lurkers

survey. Other factors highlighted as important to lurkers were: being a listener and

Figure 9.

Explanations of

lurking

behavior.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

reflector, so not being active was the natural thing to do (34.3%) and the perception

that lurking is a legitimat

e learning strategy (29.9%). Factors related to the chaotic

nature of the course and lack of confidence seemed to be less important, although

novices indicated that it took them time to adjust to the unfamiliar course structure.

For a variety of reasons (e.g. lack of confidence at the start of the course, the

way tools and language were being used, trust and comfort levels, power relations in

the course), lurkers preferred to read and view rather t

han join into a conversation.

understanding of the change process itself was also highlighted as important

the

process of transformation and the steps required to achieve it. During the lurker focus

group it was highlighted that novices might need more

time for this change process to

occur, especially in relation to building self

confidence and a sense of community in

such a large course. These perceptions were expressed by a participant in the

following blog post:

I’m new to the world of PLNs. I

certainly don’t post as much as others but I’m

learning and contributing as I go. Could I be considered a “lurker”? Perhaps,

but I’m getting more and more involved as I go on and as my comfort level

increases. . . . PLNs, despite best intentions can be qui

te cliquey (sp?) and as a

newcomer, that can be quite intimidating. Will I get more comfortable sharing

and experimenting? You bet! However, I need to do it in an environment

where I feel supported and not judged for my perceived involvement or lack

thereo

Support by facilitators was highlighted in the literature as one way to make

learners feel more at ease, but th

is was not confirmed in the end

course survey

results. Responses to statements regarding the level of advice and support received

Figure 10.

Contributing

factors to lurking behavior.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

from

facilitators and other participants in the cour

se remain ambiguous with regard to

support and feedback mechanisms. This ambiguity is highlighted in the higher

percentages of neutral responses displayed in Figure 11.

A majority of active participants (56.3%) indi

cated

that “Writing and

producing something” was “very important” in their learning and/or active

participation in the course. These same participants also

indicated that

active

production and interaction with others increased their positive learning outcomes; it

helped them to reflect, involved them in a creative process, and they wanted to give

something back to the grou

p, as shown in Figure 12.

However, th

e others with whom

they interacted did not necessarily have to be facilitators.

Figure 11.

Agreement by lurkers with

the level of support received during the course

Figure 12.

Why ac

tive participation was perceived to be important

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Motivational Issues Relevant to Networked Learning

The end

course survey highlighted factors that were important to participant

motivation. What seemed to motivate participants most was finding particularly

striking resources and information, getting involved in an online community, and the

opportunity

to learn something new. One participant highlighted, for instance, that

learning alongside self

motivated peers was what motivated her as opposed to

traditional training days where people were forced to be present. Learning how the

new environment might

improve their teaching and the learning of others was one of

the motivational factors, while the topic of discussion was another. One participant

highlighted the issues of self

evaluation, self

orientation, and self

regulation as

important in relation to

motivation in connectivist learning:

Deciding to build a self

managed PLE must be a strongly (professionally or

personally) motivated choice, and requires a high initial engagement and a

constancy during the time, to be really useful. I put the "strong

otivation

" in

the top of my list of personal requirements to build and use successfully a

PLE/Ns. That signifies also having clear objectives, before starting a learning

experience: what do I want to achieve? How long I can dedicate to do it? ...

Other pe

rsonal qualities: critical thinking, self

evaluation; self

orientation, self

regulation. I think the major challenges for people to feel comfortable learning

in PLE/Ns are related to the "self" role, in learning activity.

The relevance of learning to

everyday life was highlighte

d as important by

several learn

ers. One emphasized the importance of having choices at the start of the

learning activity to increase motivation and the need for a negotiation process

regarding content, skills, and process to ma

ke courses meaningful and relevant to

everyday life. A

ffective issues were also highlighted as motivational factors

Some

people found it particularly motivational to be learning about connectivist learning in

the company of the originators of the connect

ivism theory,

while other drew

inspiration from learning in the company of self

motivated persons with a similar

interest. They valued the opportunity to come in contact with, collaborate with, and

meet people who would help to expand their personal netwo

rk.

ritical

Literacies f

Learners Operating

n O

pen

etworked

Learning

Environment

Participants found different skills, abilities and competencies important to

learn in a complex learning environment such as in the distributed PLENK2010.

Some emph

asized the particular mindset required, while others emphasized during the

lurker focus group that novices might need more time to feel comfortable with this

change process, especially in relation to building self

confidence and a sense of

community in suc

h a large course.

One participant comment

ed:

eople need to

develop

. . . a host of new critical literacies

order to learn and

to work effectively with intelligent data, with people, and within the network.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

I see the PLE as a way to process data, ex

pand learning capacities of

participants, and grow

the network.

Participants highlighted a role for the educator in supporting this development:

for instance, by introducing them to tools and resources and by teaching them how to

critically evaluate info

rmation while using these new resources. Participants also

emphasized responsibility for their own learning and their own lives in the new

learning paradigm.

Discussion and Conclusions

The level of activity by participants in the course was particularly interesting.

Although course organizers and promoters of connectivist learning posit that actively

producing digital artifacts is an important stage in the networked learning process,

mos

t participants had a different view and participated in a different way. The large

group of silent participants, “lurkers,” who did not produce artifacts nor participate

extensively in discussions, felt that they were actively engaged in the course through

the other three activities: aggregating information, remixing of it and sharing it with

others. The percentage of lurkers was similar to that of consumers versus producers on

the Web as identified by other researchers and consequently should not be seen a

s too

low (Nielsen, 2006). Our research showed that people were actively engaged in these

other activities, although the sharing mostly took place outside the PLENK course

structure, in their workplaces or at home and sometimes after the course had finishe

because people needed time to think and reflect on the resources, information, and

communication made available during the course. Agency and activity are required in

an autonomous learning environment, but it was clear that learners have their own

ideas

on what type of activities would suit them and their lifestyles, which might not

necessarily be the same as those of the course organizers.

Some of the dimensions delineated by Bouchard (2009) clearly influenced the

level of participation and types of act

ivities learners engaged in. The

conative

factors,

related to psychological factors such as drive, motivation, and confidence, were

important. Participants who had already engaged in MOOCs before this course clearly

participated more in the active producti

on stage than novices, while they also

motivated novices by sharing new tools relevant to educational practice. Novices also

indicated their lack of confidence at participating on a worldwide stage where experts

in the field of PLEs were sharing their rese

arch; they highlighted the power

relations

as an inhibitor.

On the other hand, these high

profile contributors were mentioned by

others as a motivational factor to participation in the course. Opportunities to exploit

the expertise in the MOOC amongst wi

lling and active participants are therefore worth

exploring in future courses.

Time management, goal setting, and time availability were mentioned as the

most important

algorithmic

factors influencing people’s participation. Learners found

it hard to pace

themselves and were, especially at the start, overwhelmed by the

volume of resources and communication that needed to be managed, shaped, and

organized, even though facilitators told participants that it would be impossible to read

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

and view everything tha

t would come their way. People did make decisions about this

at a later stage and devised coping strategies with the help of others.

It seemed that the

semiotic

dimension as highlighted by Bouchard (2009), the

way in which people would access particular t

ypes of information and resources, was

very important as it was different from what participants were used to in the past.

Participants valued the new (to them) and different ways of aggregating information,

by using RSS feeds and (#) tags through social

networks and new tools. It was

important for learners to learn about new tools and find out what these could mean for

their own teaching practice. Participants helped each other to find tools that could aid

them in supporting their learning and informati

on aggregation.

The

economic

factors were also relevant to the course participants. Learners

were intrinsically motivated to participate and placed a high value on the knowledge

they developed on the course subject, Personal Learning Environments, Network

s and

Knowledge, and the new tools they could use to enhance their own teaching and work

practice, as well as the extension of their personal networks.

Additional issues played a role in learners’ participation and engagement, the

major ones being the critical literacies required to learn actively in an open networked

learning environment, such as a different mind

set and higher level of critical analysis

of resources than is the case in a more organized classroom environment. People

should clearly not have an aversion to risk and change to benefit most from learning in

a MOOC. This ability to thrive in a changing environment will be influenced by all

fou

r of Bouchard’s factors, and the research showed that there is an inter

relatedness

of Bouchard’s (2009) dimensions.

Based on analysis of the findings, it seems that to bring out the creative

potential in people and to inspire them into the production of d

igital artifacts,

dimensions of activity, engagement, and learning would have to be heightened and at

their most favorable. Heightening the level of engagement and active participation is

one of the main challenges of learning in an open networked environm

ent and one in

which educators could play a role. Educators and institutions might introduce more

openness in the curriculum by using social media and global participation outside the

boundaries of the institutional classroom to invigorate the learning ex

perience of their

students. Their participation as a critical knowledgeable other on the network could, at

the same time, enhance the thinking process of all involved.

The combination of research methods used, and especially the use of analytics,

added to

the understanding of learning in a distributed, open networked environment.

The analytics provided some clarity on the nature of the interactions between course

participants, resources and networks; however, the ethnographic approach, using

comment functi

ons on blogs and questionnaires, was indispensable in arriving at an in

depth understanding of the learning process and the learning experience of

participants. For instance, data regarding the learning experience of passive learners

(lurkers) would have b

een impossible to obtain without these measures. This paper

presents preliminary research findings and a more in

depth analysis is currently in

progress. We expect that results of these analyses will provide us w

ith indications of

the

most

favorable cond

itions for facilitating

learning for all participants in an online

networked learning environment.

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

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Rita Kop

(

Frederika.Kop@nrc

cnrc.gc.ca

) is a researcher at the NRC Canada’s IIT.

Her research focuses on learning in networked environments. Before joining NRC she

was Assistant Professor in Adult Continuing Education in the UK, after a career in

Dimensions to SDL in an Open

Networked Environment

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Dutch elementary

education. Her research interests are personalized learning, self

directed learning, and learning analytics.

Hélène Fournier

(

Helene.Fournier@nrc

cnrc.gc.ca

)

has been a Research Officer at

the Natio

nal Research Council Canada's Institute for Information Technology since

2002 and holds a Ph.D. in Educational Psychology from McGill University. Recently,

she has been engaged in researching the design and development of a Personal

Learning Environment

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

SELF

DIRECTION IN LEARNING AND PERSONALITY: THE

BIG FIVE AND NARROW PERSONALITY TRAITS

RELATION TO LEARNER SELF

DIRECTION

Jera

l R. Kirwan, John W. Lounsbury, and Lucy W. Gibson

Abstract

Based on a sample of 2102 college students,

learner self

direction

was found to

be significantly

related to four of the Big Five traits: Agreeableness

Conscientiousness

Emotional Stability,

and

Openness

—

as well as

four narrow

personality traits: Sense of Identity

Optimism

Tough

Mindedness

, and

Work Drive

Results of multiple regression analyses indicated that, after controlling for age, year in

school, and

sex

, the Big Five traits accounted

for 37% of the variance in

learner self

direction

, with

other

narrow traits accounting for an additional 15% variance. A

combination of six Big Five and

other

narrow traits account for over 52% of the

variance in

learner self

direction

It was suggested t

hat

other

personality traits may be

influencing

learner self

direction

Results a

re discussed in terms of theoretical and

methodological implications.

This paper addresses the

relationship between

learner self

direction and other

personality traits

of college students when the traits represented by the Big Five model

are differentiated from narrow personality traits. Our study draws on and extends the

work of Lounsbury, Levy, Park, Gibson, and Smith

(2009)

who reported on the

development of a perso

nality measure of

learner self

direction

and construct validity.

Before turning to their findings, we consider why this is an important topic.

Self

direction in

learning is a major topic in the field of adult learning. There

has been extensive coverage o

f the topic by theorists, researchers, and practitioners

(Brockett & Hiemstra, 1991)

Long

(2007)

has identified several themes and

measurements of self

direction in learning including psychological factors. There

have been several empirical measures cre

ated to look at different dimensions of self

direction in learning which address psychological factors such as the

Self

Directed

Learning Readiness Scale (SDLRS)

(Guglielmino, 1978)

, and more recently the

Oddi

Continuing Learning Inventory (OCLI)

(Oddi, 19

86)

and the

Personal Responsibility

Orientation to Self

Direction in Learning Scale (PRO

SDLS)

(Stockdale, 2003)

Research has shown that psychological variables are directly related to learner self

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

directedness

(Oliveira & Simões, 2006)

However, there

have been few studies that

look at

learner self

direction

specifically in relation to

personality trait

In the rationale for their study, Lounsbury et al.

(2009)

made three important

observations:

ersonality traits may influence or provide the foundati

on for

self

direction in learning

development processes

hen considered as a whole, much of the prior literature on the

relationship between

self

direction in learning

and personality traits

(Johnson, Sample, & Jones, 1988; Leitsch & Van Hove, 1998)

agmented and piecemeal.

he Big Five model of personality r

epresents an organizing scheme

for

understanding

self

direction in learning

personality trait relations.

With regard to the latter point, the Big Five model of personality traits of

Conscientious

ness, Openness, Agreeableness, Extraversion, and Neuroticism

(which

we will refer to by its inverse

—

Emotional Stability

) is widely accepted as a unified,

parsimonious model of normal personality that has been validated in many different

cultures and across

several research settings

(De Raad, 2000; Digman, 1997)

, with

supporting studies based on many different demographic and personal characteristics

of individuals

(Costa & McCrae, 1994)

The results of the Lounsbury et al.

(2009)

study indicated that there

was a

significant relationship between the five

factor model of personality and

learner self

direction

. Their findings are important in that they further elucidate the nomological

network for

learner self

direction

; in this case, that self

directed students displayed

higher levels of

Agreeableness

Conscientiousness

, and

Openness

as well as lower

levels of

Neuroticism

. These results also provide empirical support for

self

direction

learning theorists who discuss

the importance of such factors as creative

achievements, new experience, and student participation in learning projects, intrinsic

learning motivation, and self

concept

(Hassan, 1982; Reynolds, 1986)

Drawing on recent developments in personality resea

rch, it is possible to extend

the work of Lounsbury et al.

(2009)

o other personality traits that

go beyond the Big

Five model. Research in a number of areas has shown that validity can be enhanced

above and beyond the Big Five traits by considering more

narrow personality traits,

which are defined as either subscales of the Big Five or as traits not encompassed by

the Big Five model. For example, Lounsbury, Sundstrom, Gibson, and Loveland

(2003)

found that

ggression and

Work Drive

added substantial var

iance to the

prediction of academic performance of middle and high school students beyond the

Big Five traits. Paunonen and Nicol

(2001)

found that narrow traits, such as

elf

iscipline,

traightforwardness, and

odesty, added significant incremental var

iance

beyond the Big Five when predicting 12 different criteria, including grade point

average, blood donations, absenteeism, and traffic violations. Also, Paunonen and

Ashton

(2001)

found that NEO

Conscientiousness

related subscales of

chievement,

elf

iscipline,

ompetence, and

utifulness as well as the

Openness

related subscale

deas added significantly to the prediction of collegiate GPA above and beyond the

Jackson Personality Inventory

Conscientiousness

scale. Accordingly, the purpose of

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

the p

resent study was to investigate whether

other

narrow personality traits are related

learner self

direction

and to see if they contributed incremental validity to the

prediction of

learner self

direction

above and beyond the Big Five. The narrow traits

we examined were

Sense of Identity

Optimism

Tough

Mindedness

, and

Work Drive

These traits are not part of current Big Five taxonomies and have been found to be

related to important outcome criteria for college students including grades,

satisfaction, an

d intention to withdraw from school

(cf.

Lounsbury, Sadaurgas, &

Gibson, 2004; Lounsbury, Sadaurgas, Gibson, & Leong, 2005)

In the present study, ou

r focus was on

learner self

direction

as an individual

differences variable that can be represented on a

continuum from low to high.

were not interested in representing

learner self

direction

as a categorical or nominal

variable representing an

identity

status such as state of foreclosure, diffusi

on,

moratorium, or achievement.

With respect to Brockett a

nd Hiemstra’s

(1991)

two

dimension, self

direction in learning taxonomy,

our

learner self

direction

construct

corresponds to their learner self

direction construct.

Consistent with prior

conceptualizations of self

direct

ion in

learning

(Brockett, 1983;

Brockett & Hiemstra,

1991; Costa & Kalick, 2003

)

, we conceptualized and measured learner self

direction

as a personality construct reflecting an

individual’s

preference to be in charge of their

learning process; ability to conceptualize, plan, implement, a

nd evaluate their

academic experience; and disposition to be goal

oriented and to work independently or

in group settings with little guidance.

We chose to study personality

learner self

direction

relationships among college

students for several reasons. T

he college experience is regarded as providing “many

opportunities for students to develop, among other things, personal and professional

identity

”

(Hamrick, Evans, & Schuh, 2002, p. 135)

. As Madison

(1969)

observed,

college represents a unique and highly

appropriate setting for studying

Identity

Moreover, for those individuals who go to college directly from high school, the

college experience occurs during a key developmental period for

Identity

development

(Waterman, 1985, 1993)

, and it is regarded as p

laying a “critical role in

identity

formation”

(Nakula, 2003, p. 9)

. We examined

three

research questions:

How much of the variance in

learner self

direction

can be accounted for jointly

by the Big Five traits?

Are the narrow traits

of Sense of

Identity, Optimism, Tough

Mindedness, and

Work Drive related to learner self

direction?

Do the narrow traits add incremental validity beyond the Big Five traits in

predicting

learner self

direction

Method

Participants

A total of 2102 students enrolled i

n an introductory psychology course and a

First

Year Studies program, at a large, public southeastern U. S. state university

volunteered to participate in this study. Demographic characteristics of the sample

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

were:

Gender

68% female (

32% male); Year in

School

79% freshmen, 15%

sophomore, 3% junior, 3% senior; Age

3% under 18, 81% 18

19, 8% 20

21, 3% 22

25, 2% 26

30, and 3% over 30.

Procedure

After obtaining human subjects approval from the university’s Institutional

Review Board, participants were s

olicited to take a personality inventory on

line.

Upon completion of the inventory, participants were provided a feedback report

summarizing their personality characteristics and implications for a variety of areas

related to being a student, including are

a of study, social life, managing stress, study

habits, living situation, and using campus resources. Students in the introductory

psychology course were offered extra credit for participation. All data were collected

between September 1, 2004 and December

30, 2004.

Personality Measure

The personality measure used in this study was the Resource Associates’

Transition to College inventory (RATTC). The RATTC is a normal personality

inventory contextualized for late adolescents

(Jaffe, 1998)

and adults through high

school and college. It measures the Big Five Traits of Agreeableness,

Conscientiousness, Emotional Stability, Extraversion, and Openness as well as other

“narrow” personality traits and

learner self

direction

. Scale development, n

orming,

reliability, criterion

related validity, and construct validity information for the R

ATTC

can be found in Lounsbury and

Gibson

(2010)

Findings from the abov

e studies demonstrated

that the RATTC constructs are

internally consistent and display gen

erally high convergence with common traits on

other, widely used personality inventories, including the 16 PF, NEO

R, and the

Myers

Briggs Type Inventory (e.g., the RATTC measure of Extraversion correlates

.77 with NEO

R measure of Extraversion). Mor

eover, the Big Five measures of the

RATTC significantly predict collegiate academic performance and withdrawal

intention

(Lounsbury, Sundstrom, Gibson, & Loveland, 2003; Ridgell & Lounsbury,

2004)

. A

n adult version of the RATTC has been found to be relate

d to job

performance, job satisfaction, and career satisfaction in a wide variety of occupations

in many different business and industry settings

(Lounsbury & Gibson, 2010)

Big Five and narrow traits assessed.

The Big Five and narrow traits measured

in t

his study, along with brief descriptions and their coefficient alphas, are listed

below:

•

Agreeableness

: being agreeable, participative, helpful, cooperative, and

inclined to in

teract with others harmoniously

(coefficient alpha = .81)

•

Conscientiousnes

: being conscientious, reliable, trustworth

y, orderly, and

rule

following

(coefficient alpha = .78)

•

Emotional Stability

: overall level of adjustment and emotional resilience in the

face of stress and pressure. We conceptualized this as the inverse of

euroticism

oefficient al

pha = .83)

•

Extraversion

tendency to be sociable, outgoing, gregarious, warmhea

rted,

expressive, and talkative

(coefficient alpha = .84)

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

•

Openness

receptivity and

Openness

to change, innovatio

n, new experience,

and learning

oefficient alpha = .7

•

Sense of Identity: knowing one’s self and where one is headed in life, having a

core set of beliefs and values that guide decisions and actions; and having a

sense of purpose

oefficient alpha = .77)

•

Optimism

: having an optimistic, hopeful outlook concerning prospects, people,

and the future, even in the face of difficulty and adversity as well as a tendency

to minimize problems and

persist in the face of setbacks (c

oefficient alpha =

.83)

•

Tough

Mindedness

appraising information and making decisions based on

logic, facts, and data rather than feelings, se

ntiments, values, and intuition

oefficient alpha = .75)

•

Work Drive

: being hard

working,

industrious, and inclined to put in long hours

and much time

and effort to reach goals

and achieve at a high level (c

oefficient

alpha = .85)

Learner self

irection

tems.

The ten items comprising the learner self

direction subscale of the Resource Associates Transition to College (RATTC)

inven

tory are listed belo

w. I

tem responses were made on a five

point Likert scale:

1=Strongly Disagree; 2= Disagree; 3=Neutral/Undecided; 4=Agree; 5=Strongly

Agree.

I regularly learn things on my own outside of class.

I am very good at finding out answers on my own for

things that the

teacher does not explain in class.

If there is something I don’t understand in a class, I always find a way to

learn it on my own.

I am good at finding the right resources to help me do well in school.

I view self

directed learning based on

my own initiative as very important

for success in school and in my future career.

I set my own goals for what I will learn.

I like to be in charge of what I learn and when I learn it.

If there is something I need to learn, I find a way to do so right awa

I am better at learning things on my own than most students.

10.

I am very motivated to learn on my own without having to rely on other

people.

For the present sample, the coefficient alpha for the above RATTC was .85.

Demographic V

ariables

The age

and

gender

of students were assessed using categorical items. In

addition, we used two nontraditional student subgroups provided by the Nontraditional

Student Resource Guide (University of Oregon, 2005) to ask respondents whether

either of these character

istics applied to them:

•

Over the age of 25

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

•

Returning to or starting college after a long break

Results

Descriptive statistics and intercorrelations

among the study variables are

displayed in Table 1. All of the Big Five personality traits are correlated significantly

and positively with

learner self

direction

, except for

Extraversion

. Specifically, in

descending order of magnitude, the correlations

with

learner self

direction

were:

Openness

(

= .

< .01),

Emotional Stability

(

= .

< .01),

Conscientiousness

(

= .

< .01),

Agreeableness

(

= .

< .01),

Extraversion

(

= .01,

.01),

and t

other

narrow personality traits also

correlated significantly with

learner self

direction

, with the largest magnitude correlation observed for

Work Drive

(

= .

.01), followed by

Optimism

(

= .31,

< .01),

Sense of Identity

(

= .30,

< .01), and

Tough

Mindedness

(

.07,

< .05

Table 1

Descriptive Statistics and Intercorrelations for the Personality and

Satisfaction V

ariables

_____

__________________________________________________________

____________________

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1) Agreeableness

---

.16

.28

.02

.19

.34

.33

.31

.26

.21

(2) Conscientiousness

---

.13

.06

.05

.28

.23

.11

.33

.20

(3)Emotional

Stability

---

.24

.07

.46

.59

.14

.09

.20

(4) Extraversion

---

.01

.26

.34

.15

.01

(5) Openness

---

.21

.18

.16

.41

.43

(6) Sense of Identity

---

.67

.22

.36

.30

(7) Optimism

---

.16

.26

.31

(8)Tough

Mindedness

---

.23

.07

(9) Work Drive

---

.49

(10) SDL

---

Mean

3.74

3.38

3.17

3.54

3.52

3.96

4.01

2.32

3.18

3.29

Standard Deviation

.62

.50

.69

.66

.59

.62

.57

.65

.62

.59

_______________________________________________________________

______

Note:

= 2102

; medial effect size = 25.5; range of effect size =

.07 to .49

Correlations > .09 or

.09 are significant at the

< .01 level.

Correlations > .05 and < .09 or <

.05 and >

.09 are significant at the

< .05 level.

To evaluat

e research questions 1 and 3

, we performed a series of regression

analyses with

learner self

direction

serving a

s the criterion variable and three

demographic variables which have been linked to

Identity

—

age,

sex

, and year in

school (which in the present study correlated .11 (

< .01), .14 (

< .01), and .05 (

.05), respectively, with

learner self

direction

)

—

serving as control variables by

entering them as a set on the first step of each regression analysis. In the first analysis,

the Big Five traits were regressed on

learner self

direction

in stepwise fashion and all

five significantly entering the equatio

n, accounting for 37% of the variance in

learner

self

direction

beyond the 3 demographic variables, as can be seen in the first

regression result in Table 2.

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Table 2.

Regression

Analysis f

Learner Self

Direction with Age, Year in School,

Gender E

ntered First Followed by t

he Big Five Traits Entered Stepwise

Step

Variable(s)

Multiple

Change

Age, year in school, and

gender

.172**

.030**

Emotional Stability

.506**

.256**

.226**

Conscientiousness

.563**

.317**

.061**

Extraversion

.586**

.343**

.026**

Openness

.603**

.362**

.020**

Agreeableness

.608**

.370**

.007**

Note:

= 2102 *

<.05 **

<.01

To answer the question of whether the four narrow traits contributed incremental

variance in the prediction of

learner self

direction

beyond the Big Five, the following

regression procedure was employed. The three demographic variables were entered as

a s

hierarchically on the first step, followed by the set of Big Five traits on the

second step;

the

narrow traits were allowed to enter in stepwise fashion. As

can be

seen in the results in Table 3

, the demographic and Big Five variables accounted for

37% of the variance, with

Optimism

adding an additional 14% of the variance (

.01), and

Work Drive

contributing an additional 1.5% of the variance (

< .01) in

learner self

direction

Sense of Iden

tity and Tough

Mindedness did not account for

any significant variance in learner self

direction.

Table 3.

Regression

Analysis f

Learner Self

Direction with Age, Year i

n School, And

Gender

Entered First

The

Big Five Traits Entered Second as A Set,

Followed b

Narrow Traits Entered Stepwise

Step

Variable(s)

Multiple

Change

Age, year in school, and

sex

.172**

.030**

Big five traits

.608**

.370**

.340**

Optimism

.717**

.514**

.144**

Work Drive

.727**

.529**

.015**

Note:

= 2102 *

<.05 **

<.01

As can be seen in the

third regression results in Table 4

, when the Big Five and

narrow traits were allowed to enter the regression in stepwise fashion after the

demographic variables,

Optimism entered first, contribut

ing an additional 44% of the

variance (

< .01); Work Drive entered next, adding 3% (

< .01), followed by

Conscientiousness (

change = 1.4%,

< .01), Emotional Stability (

change =

.008%,

< .01), and Tough

Mindedness (

change = .005%,

< .01).

These five

personality traits jointly accounted f

or over 50% of the variance in learner self

direction

yond that accounted for by the

demographic variables of age

, year in

school, and gender.

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Table 4.

Stepwise

Regression Analysis f

Learner Self

rection with Age, Year in

School, and Gender

Entered First

; Then a

ll Personality Traits Entered Stepwise

Step

Variable(s)

Multiple

Change

Age, Year in School, and

Gender

.172**

.030**

Optimism

.689**

.474**

.444**

Work Drive

.711**

.506**

.032**

Conscientiousness

.721**

.519**

.014**

Emotional Stability

.727**

.527**

.008**

Tough

Mindedness

.730**

.532**

.005**

Note:

= 2102 *

<.05

<.01

Discussion

The findings of this study

provide support for Lounsbury

et al.’s

(2009)

summary “...of the importance and richness of the self

directed learning construct and

provide strong support for its role as a personality t

rait...” (p. 417).

All of the Big

Five traits

correlated significantly with

learner self

direction

, except

for Extraversion

The significant, positive relationships between

learner self

direction

and

Emotional

Stability

are consistent with Lounsbury, et al.’s correlational

findings of a negative

relationship between

Neuroticism

and

learner self

direction

. Moreover, the results of

the present study indicate that the Big Five traits jointly explained a substantial

amount of variance in

learner self

direction,

which provides a

dditional support for the

robustness of the Big Five model

(e.g., De Raad, 2000; McCrae & Costa, 1997, 2003)

It appears that additional variance in

learner self

direction

can also be accounted

for by

other

narrow personality traits. The results of the sec

ond regression analysis

indicate that the traits of

Optimism

and

Work Drive

added incremental variance

beyond the Big Five in predicting

learner self

direction

. Moreover, the results of

regression analysis indicate that the narrow traits of

Optimism

and

rk Drive

entered the regression equation to predict

learner self

direction

before any of the Big

Five traits. At this stage of research development, we would not conclude that any one

of the personality traits studied is more strongly related to

learner s

elf

direction

than

other traits, but the

moderate

magnitude of the

Optimism

learner self

direction

correlation is noteworthy and would be a prime candidate for replication and

explication by future research. Consistent with recommendations in

other resear

domains to use multidimensional composites

(Paunonen & Nicol, 2001

; Schneider,

Hough, & Dunnette, 1996

)

, comprised of both broad traits such as the Big Five and

narrow personality

measures, to maximize validity

, we suggest that future research on

the

lationship

s between learner self

direction

and other personality traits

consider

both the full set of Big Five traits as well as narrow traits of interest which need not be

limited to the small number of narrow traits we considered.

The generalizability of

other

personality

traits and learner self

direction

across

different domains of demographic and social role characteristics augurs well for future

self

direction in

learning theory development which seeks to establish generalized

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

construct relations involving personality traits, and it also provides food for thought

concerning a crucial unresolved issue noted by Clancy and Dollin

ger

(1993)

and

framed here as: What

is the causal direction of

other personality traits in relation to

earner self

direction

? That is, do

other

personality traits influence or contribute to

learner self

direction

, or does

learner self

direction

influence

other

personality traits,

or is the relationship bi

directional? Attempts to resolve this issue should i

nvolve a

longitudinal design, which was not utilized in either Lounsbury et al.

(2009)

or the

present investigation, and may involve measurement of college student experiences

and activities through which personality is manifested. As but one example, it m

ay be

that higher levels of

Conscientiousness

and

Work Drive

lead to more successful study

habits and academic performance, which may, in turn, lead to higher levels of

learner

self

direction

Nevertheless, there are several considerations that point t

oward a conceptual

model emphasizing the primacy of personality traits and portraying personality traits

as leading to

learner self

direction

. From a lifespan

developmental perspective

(e.g.,

Berger, 2001; Erickson, 1980)

identity

issues emerge primarily

in adolescence,

whereas personality traits, including constructs corresponding to the Big Five, have

been reliably studied for children as young as age 3

(van Lieshout & Haselager, 1993,

1994)

; thus, it is not unreasonable to consider

other

personality tra

its as preceding

learner self

direction

. Moreover, personality traits are typically regarded as being

relatively invariant or consistent over time and across situations and environmental or

situational characteristics

(e.g., Pervin & John, 1997)

In view o

f the above, we suggest that if personality traits are relatively consistent

for students across situations and over time, and if learn

self

direction changes more

across situations and over time, the most logical interpretation of why the personality

ait

learner self

direction

relationship is relatively consistent within and across such

disparate factors as age and returning to college after a long break is because the

personality traits are driving the relationship, which implies that

other

personali

traits are affecting

learner self

direction

, not that

learner self

direction

is influencing

other

personality traits. This is a conceptual model which should be more rigorously

evaluated by future research, but should it prove to be even partially true,

it would

have major implications for those theories of

self

direction in learning

which place

primary emphasis on the role of personal experiences and environmental determinants

of college student self

direction. Such a model would not rule out the role

experiential and environmental factors in self

direct

ion in

learning for college

students; rather, it would mean that personality traits, even traits measured in high

school, may influence collegiate activities and experiences which may, in turn,

influe

nce the

learner self

direction

of college students. It may be that personality

traits, not academic and personal experiences, are the major determinants of college

student

self

direction in learning

Directions for Future Research

There are a number

of interesting

areas for future research that

could clarify and

extend the present findings. In addition to the need for replication on different

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

samples, research could be conducted on how the Big Five and narro

w personality

traits relate to S

ense of

Iden

tity

and

learner self

direction

. Another topic to investigate

further is the relationship between age of college students and learner self

direct

ion

As mentioned earlier, perhaps the most important need for future research is to utilize

longitudinal resea

rch designs to help clarify the direction of causality for personality

traits vis

vis

learner self

direction

and to try to determine how these linkages are

established. For example, do individuals who are more optimistic engage in new

learning activities

than mo

re pessimistic individuals, with optimism helping to

facilitate self

irection

Hopefully, subsequent research in this area can assess the

linkages among

learner self

direction

Big Five and narrow traits, and

a variety of

important criteria in the

college student domain, including grades

(e.g., Furnham,

Chamorro

Premuzic, & McDougall, 2003;

Lounsbury, Sundstrom, Loveland

et al.,

2003)

, life satisfaction

(Lounsbury

et al., 2004)

, dropout

(Heilbrun, 1962, 1965)

life

satisfaction, and subjective well

being

(DeNeve & Cooper, 1998)

Study Limitations

There are two primary limitations of the current study that should be

acknowledged. First, this study was limited to a four

month interval in time for a

single year in a single geographic area at a large,

public university, leaving open the

question of generali

zability to other time periods,

geographic areas, and types of

universities. Second, most of the study participants were underclassmen; thus, we do

not know if the results would genera

lize to samples

from other educational levels

Conclusions

The results of the present study

indicate

that

part of

the Big Five traits as well as

three of

the four

other

narrow traits measured in this study were each related to

learner

self

direction

, with

other

narr

ow traits adding incremental validity to the Big Five and

accounting for

substantial

variance in

learner self

direction

on their own. In

combination, the Big Five and narrow traits accounted for more than

half

of the

variance in

learner self

direction

and a composite of six traits was found to be

substantially related to

learner self

direction

for eight different subgroups of students

representing different categories of nontraditional students and

student gender

. Taken

as a whole, the present findings

were interpreted as, in part, confirming and extending

the results of Lounsbury et al.

(2009)

regarding the Big Five and

learner self

direction

demonstrating the generalizability of personality trait

learner self

direction

relationships across a variety

of different demographic and

personal subgroups of

students

and providing some clues that the direction of the causal arrow may be from

other

personality traits to

learner self

direction

In conclusion, it is clear that

learner self

direction

has

manifold

connections to

other

personality traits and is not clearly associated with just one of the Big Five traits.

In a sense, this pattern of multiple connections to

personality is consistent with

the

diverse factors

learner self

direction

has been linked to in

the theoretical literature, as,

for example, the

six vectors of college student

development that Chickering and

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Reisser

(1993)

posit as leading to

identity

establishment for college students.

Hopefully, further research

will

extend and clarify the nomolog

ical network of

other

personality traits and

learner self

direction

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Directed Learning

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Jeral Kirwan

(

[email protected]

) is a doctoral student in the Adult Learning Program

at the University of Tennessee, Knoxville. His research interests include self

direction

in learning, personality, and psychometrics.

John Lounsbury

(

jlounsbu@

utk.edu) is President

, Resource Associates, Inc., and Full

Professor, Department of Psychology, University of Tennessee. Dr. Lounsbury

is a

Fellow

the

American

Psychological

Association

and

licensed

Industrial/Organizational Psychologist.

Self

Direction in Learning and Personality

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Lucy Gibson

is Vice President of Resource Associates, Inc. Dr. Gibson is a Licensed

Industrial/Organizational Psychologist who has many years of

experience in the areas

of test development, test validation, and implementation of selection testing programs.

She has taught at the University of Tennessee and Tusculum College.

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

THE ROLE OF SELF

DIRECTED LEARNING IN THE WORK OF

COMMUNITY LEADERS

Leatrice Turlis Phares and Lucy Madsen Guglielmino

Abstract

This study was designed to examine self

directed learning readiness of volun

teer

community

leaders and to explore their use of

self

directed learning in their

community leadership roles. The mean for this sample on Guglielmino’s (1978)

SDLRS (Learner Preference Assessment) was significantly higher than the general

population mean.

depth interviews with 10 of the 131 subjects revealed extensive

self

direc

ted learning

contributing to the participants’ community leadership efforts

and a strong belief that ongoing learning is essential to perform well as a community

leader

The increasing complexity of our society, our work, and expanding technology

place more demands on those who volunteer for community leadership roles.

In 1996

Kotter

stated, “. . . by any objective measure, the amount of significant, often

traumatic, chan

ge in organizations has grown tremendously in the past two decades”

(p. 3), and the change h

as continued to escalate

O’Connell (2006) notes,

“The

problems of contemporary society are more complex, the solutions are more involved

and the satisfaction more

obscure, but the basic ingredients to progress are still the

caring and the resolve to make

things better” (

p. 7).

Community leadership is defined

as assisting the public and private non

profit sectors in meeting the changing needs of

local communities, o

rganizations and citizens (Kouzes & Posner, 1995). Today’s

society expects its leaders to take the initiative and devise goals and strategies to solve

our increasingly complex community problems, working effectively both individually

and within groups. Cl

ark (1999) asserts that leaders must be creative problem solvers

who work in a team atmosphere and are able to organize resources to accomplish tasks

with maximum efficiency. They need to be flexible, able to assess situations quickly

and

accurately and to

create appropriate

goals. Kouzes and Posner (1995) surveyed

several thousand business and government executives and found that forward thinking

and a sense of direction were other important leadership characteristics; and Kotter

(1998) found that the mos

t notable trait of great leaders is their quest for learning.

Voluntary community leaders step forward to take responsibility for community

problems, often with little or no formal preparation, gathering information and

marshalling resources to address new

issues and challenges. The characteristics and

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

actions of community leaders described in the literature suggest that they are highly

self

directed learners.

Self

directed learning has been described as a process in which individuals

take the initiative,

with or without the help of others, in diagnosing their learning

needs, formulating learning goals, identifying human and material resources for

learning, choosing and implementing appropriate learning strategies, and evaluating

learning outcomes (Knowles,

1975). Tough (1971) found that 98% of adults are

involved in self

planned learning, with a mean of 8.3 projects a year, each

averaging

8.16 hours, and several recent studies have reinforced his findings (Davis, Bailey,

Nypaver, Rees, & Brockett,

2010

;

Gug

liel

mino et al.,

2005).

According to Merriam,

Caffarella, and Baumgartner (2007), “Guglielmino has provided the most

used

operational definition for self

directed learning” (p. 121).

glielmino (1978)

described a highly self

directed learner based on her

Delphi study of experts in self

directed learning:

A highly self

directed learner is one who exhibits initiative, independence, and

persistence in learning; one who accepts responsibility for his or her own

learning and views problems as challenges, not

obstacles; one who is capable

of self

discipline and has a high degree of curiosity; one who has a strong

desire to learn or change and is self

confident; one who is able to use basic

study skills, organize his or her own time, set an appropriate pace for

learning,

and develop a plan for completing work; one who enjoys learning and has a

tendency to be goal

oriented. (p. 73)

A growing body of literature supports a link between self

directed learning and

attainment of or performance in leadership roles (Boy

ce, 2004; Durr, 1992; Connelly,

2004; Guglielmino, 1996; Guglielmino & Guglielmino, 1983, 2008; Kandarian, 2004;

Roberts, 1986).

Self

Directed Learning Readiness Scale (SDLRS)

levels are even

higher among top entrepreneurs in the U.S. who, like community

leaders, often have

fewer guidelines such as corporate policies to guide or restrict their actions

(Guglielmino & Klatt, 1994).

Many leaders in the workplace, whether in business, education, or other areas,

are also community leaders. It appears that the

processes of community problem

solving and self

directed learning are analogous, as are the characteristics of effective

community leaders and the characteristics of highly self

directed learners.

Imel

(1999) states that there are those who participate in

self

directed learning for the

process of community problem solving.

However, the use of self

directed learning by

volunteer community leaders has

not

previously

been investigated in depth. Ta

ylor

(2002) raised the issue

that “there is very little in the

literature that analyzes exactly

how self

directed learning is happening, the dynamics of learning in these contexts or

the differences between learning as an individual for personal reasons and learning as

an individual member of a group working for a co

mmon cause” (p. 44). Determining

the levels of self

directed learning readiness of community leaders and exploring

whether they use self

directed learning in their leadership roles

(and, if so, how) can

enhance our understanding of the process of communit

y leadership and provide

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

valuable insights to improve the limited professional development available for

community leaders.

Purpose and Research Questions

This study was designed to examine the self

directed learning readiness of

community leaders

and to

explore their

use

self

directed learning in their

community leadership roles. Three research questions and one hypothesis guided the

study:

1. What is the mean level of self

directed learning readi

ness of community leaders

and

how does it compare t

o the readiness levels of other groups?

2. What types of learning projects did community leaders participate in during the 12

month period prior to the study?

3. Did the community leaders use self

directed learning projects to carry out their

community l

eadership roles? If so, to what extent?

One quantitative research hypothesis was posed to investigate the first research

question. The second research question was

explored through documentation and

analysis of learning projects

in structured in

depth inte

rviews

The third research

question was explored through analysis of the responses to open

ended questions

incorporated into the interview.

Null

Hypothesis:

There is no significant difference in the level of self

directed

learning readiness of community leaders as measured by the Self

Directed

Learning Readiness Scale (

SDLRS

) and the mean

SDLRS

score for U.S.

adults.

Delimitations and Limitations

This study

limited to volunteer leaders of community service

organizations, specifically (a) board members of the Leadership Broward Foundation

in Fort Lauderdale, Florida; Leadership Miami in Miami, Florida; Leadership Palm

Beach County in West Palm Beach, Flo

rida; and (b) Rotarians who have a leadership

role in Rotary District 6990 and live in Broward, Miami

Dade

and Monroe Counties

in Florida.

The

convenience sample also constitutes a limitation. The

primary researcher

is a member of two of the organization

s studied, which creates an advantage in terms

of access to participants, but may affect the content of participant response and

interpretation of results. However, the researchers strove for objectivity and an

additional professional educator reviewed the

transcripts and data analysis.

Method

To assess the readiness for self

direction in learning among the community

leaders and compare it to the means of other groups, the SDLRS (Guglielmino, 1978)

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

was used. A modification of Tough’s (1971) interview

schedule was used to gather

data on the learning projects of a selected sample of the community leaders. Open

ended questions were added to the interview to further explore the use of self

directed

learning in community leadership roles. The data collecti

on instruments are described

below.

Instruments

Self

Directed Learning Readiness Scale

The

SDLRS

is the most often used

quantitative measure of self

directed learning (Merriam, Caffarella, & Baumgartner,

2007).

It is a 58

item, five

point Likert

type scale that measures the attitudes, values,

and abilities of learners relating to their readiness to engage in self

directed learning at

the time of their response. This readiness is assessed as a total score, which is then

converted into bands of

hig

h, above average, average, below average

and

low

levels

of readiness (Guglielmino

& Guglielmino

, 1991). The

SDLRS

is referred to in test

settings as

the

Learning Preference Assessment

(LPA

). The reliability has most often

been assessed through measures of

internal consistency; estimates normally range

from .87

.92 (

Delahaye & Choy, 2000

ontent validity of the

SDLRS

was established by Guglielmino’s (1978)

development process, which used a modified Delphi technique that involved a panel of

experts in th

ree rounds of surveys to identify the characteristics of a highly self

directed learner. Fourteen researchers known for their work in the area of self

direction in learning participated. Among them were Malcolm Knowles, Allen Tough,

Cyril Houle, B. Frank B

rown, Arthur W. Chickering, Wilbert J. McKeachie, a

Morris Weitman (Guglielmino,

1997)

. The vast majority of studies have supported the

reliability and validity of the instrument (for example,

Chuprina &

Durr, 2006

;

Connolly, 2004;

Delahaye & Smith, 1995

; Durr, 1992; Finestone,

1984; Graeve, 1987;

Hassan, 1981

;

Liddell,

2007

;

Long & Agyekum, 1984;

McCune, 1988;

McCune &

Guglielmino, 1991;

Muller, 2007

;

Oliviera &

Simões, 2006

;

Posner, 1989

90; Zsiga,

2007). T

here has been some criticism (

Brockett, 1985

; F

ield, 1989)

, responded to by

Long (1989), McCune (1989), and Guglielmino (1989)

A co

mprehensive review by

Delahaye and

Choy (

2000) concluded

, “There has been extensive support for the

[SDLRS]

LPA

in the literature as an accurate and useful instrument for measuring

readiness for self

directed learning” (p. 2).

ough’s interview schedule with additional o

ended q

uestions

modification of Tough’s (1971) interview s

chedule was used to answer

the research

questions regarding the types of learning projects the community leaders had

participated in over the 12 months preceding their interviews. Numerous

studies using

Tough’s Interview Schedule have been conducted (for example, Brasfield

, 1984,

Coolican, 1975; Davis et al.

, 2010; Estrin, 1986;

Guglielmino et al.

, 2005;

Hiemstra,

1976; Penland, 1978, 1979; Peters & Gordon, 1974; Ralston, 1981). Although there

have been variations in both the total number of learning projects and in the total

perce

ntage of self

planned projects, the findings from the original Tough investigation

have largely been substantiated (Brockett & Hiemstra, 1991).

odification of the interview s

chedule for this study involved the addition of

open

ended questions. One broad q

uestion was added specifically to explore learning

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

projects related to the performance of the subjects’ community leadership roles: “Let’s

take a little time now to talk specifically about learning and your community

leadership role. Would you tell me abou

t any new learning required for your

community leadership role?” Appropriate follow

up questions were asked to fully

explore this topic.

Procedures

Assessment of SDL

eadiness

The convenience sample for the assessment of

readiness for self

directed lear

ning consisted of v

olunteers who held

leadership roles

in community organizations. The participants

were recruited from the specified

groups

until a usable sample size was obtained. Power analysis indicated that at least 128

subjects were needed to obtain

a power of .80 with a medium effect size (

.50) with

an alpha of .05.

After approval

by the Institutional Review Board, the

primary

researcher

requested letters of support

for the study

from leaders of the targeted organizations and

then contacted the

executive directors of Leadership Broward Foundation, Leadership

Miami, and Leadership Palm Beach County to gain permission to attend a board

meeting and explain the research

study. A

fter expl

aining the study, she distributed

the

data collection material t

o board members who agreed to participate

at that time

. The

materials in

cluded: (a) Institutional Review Board

consent forms, (b) the Learning

eference Assessment (SDLRS),

, and (d)

a self

addressed,

stamped envelope to return the

completed questionnaire.

The researcher used the District 6990 Rotary International 2004

2005 Official

Directory as a guide to identify the Rotarians in a District Rotary 6990 leadership role

(Benson, 2004).

She then

attended major Rotary

District 6990 e

vents and asked

Rotary leaders

if they would like to volunteer to complete the

LPA

questionnaire.

Procedures for administration were the same as described for the leadership groups,

except that these participants were offered the option to complete the que

stionnaire

and demographic sheet at that time or return it to the researcher at a later date. The

materials were distributed to 172 potential participants. The

primary

researcher placed

a follow

up telephone call to participants who had not returned their

questionnaires

within two weeks. Seventy

one participants chose to return the material by mail, while

60 participants completed the material onsite, resulting in a 76% response rate for the

survey (131 of 172).

Exploration of learning p

rojects

Once the

forms were administered and

the completed forms were returned, t

he researcher chose a

subsample of ten

community leaders to represent a cross

section of ethnicity, gender, age, education,

and the four identified organizations.

SDLRS

(

LPA

) scores were no

t computed before

the individuals were selected and interviewed. The ten interviews were based on a

modification of Tough’s Interview Schedule to obtain direct information about the

types of learning projects the community leaders participated in over the

12 months

preceding the interviews.

The interviews, conducted by the

primary

researcher, lasted from one to two

hours; they took place at locations mutually agreed upon by the researcher and the

participants. Participants were assured of the confidential

ity of their responses

and

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

their right to withdraw at any time

and

were

asked to sign a consent form. All

participants were asked the same questions in the same order

using both fixed choice

and open

ended questions. The open

ended questions were designed

to explore in

detail the learning projects of the community leaders

and a final open

ended question

was add

ed in an attempt to determine if and how

community leaders used learning

projects to carry out their community leadership role. If early responses

indicated that

they did, follow

up questions were used in an attempt to determine the extent to which

this had occurred. The participants’ replies were audiotaped and transcribed by the

researcher.

To ensure accuracy

of the acquired data, the researcher al

so used member

checking, asking the participants to review their transcripts for accuracy and make any

app

ropriate changes (Glesne, 1999)

Profile of Respondents to the LPA

There were 71 males and 60 females in the study. The majority (81.7%) of the

participants were Caucasian

with 9% African American, 6.1 % Hispanic, 1.5% Asian

or Pacific Islander and 0.8% American Indian or Alaskan Native

. Most were between

the ages of 36 and 65

, with the largest number being 56

65 years old. Everyone had

at leas

t some college, with most having some graduate education. Almost all reported

that their employment level was professional or managerial.

Data Analysis

The completed

SDLRS/LPA

questionnaires were scored using the instructions

provi

ded by the author

(Guglielmino & Guglielmino, 1991

) and the mean score was

calculated. The

LPA

mean score of the community leaders was compared to the

general adult pop

ulation mean score of 214,

to the mean of

a meta

analytic study of

research using the

LPA

with adults in 2

9 different studies from 1977

1987 (McCune,

Guglielmino, & Garcia, 1990)

, and to a sample

of top entrepreneurs

(Guglielmino &

Klatt, 1994)

tests were used for the comparisons

. The quantitative items from

Tough’s Interview Schedule were tallied and the

data described to develop an

understanding of the extent and types of learning projects conducted by community

leaders

In analyzing the

open

ended questions

that were added, t

he researcher

identified the most common responses and documented them with su

pporting

quotations. Two researchers independently conducted the analysis.

Findings

Self

Directed Learning Readiness

Mean score of s

ample.

The mean

LPA

score of the sample of 131 participants

was 245.09 with a standard deviation of 19.04. The lowest score was 187 and the

highest score was 285. According to the conversion table

(Guglielmino &

Guglielmino, 1991

), the participants’ mean score of 245.09 rank

ed in the 87th

percentile and converted into a readiness level of

above

average

. No participants

scored in the low readiness level. Two participants scored in the below average level

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

and 20 participants scored i

n the average level. Eighty

three percent (83

of the

participants

cored in the above average and

high level

. Fifty

four scored in the above

average level and 55 scored in the high level.

Hypothesis t

esting.

The

null

hypothesis tested in this study was that there is

no significant difference in th

e levels of the self

directed learning readiness of

community leaders as measured by the Self

Directed Learning Readiness Scale

(SDLRS/LPA)

and the mean score for US

adults and specified groups.

The

LPA

mean

score of community leaders (M = 245.09, SD = 19

.04) was significantly higher than

the

LPA

mean score of the general population (M = 214, SD = 25.59),

(130) = 18.69,

p<.001.

The Cohen’s

for this

comparison was 1.21,

a large effect size (Cohen,

1988).

The

LPA

mean score of community leaders was also

significantly higher than

the

LPA

mean score of adults in 29 different studies from 1977

1987 (M = 227.7),

(130) = 10.46, p<.001.

The

eta

squared for this co

mparison was 0.45699,

a large

effect size (Cohen, 1988). Finally, the

LPA

mean score of communit

y leaders was

significantly lower than the

LPA

mean score of top entrepreneurs (M = 248.6), SD =

18.74),

(130) = 3.51, p =.037.

The Cohen’s

for this

comparison was 0.18,

a small

effect size (Cohen, 1988).

Con

sequently, the null hypothesis wa

s not acce

pted. These

results support the alternative hypothesis that mean

LPA

scores for community leaders

differ from mean

LPA

scores of the general population, adults in 29 different studies

from 1977

1987, and top entrepreneurs.

Interviews

Profile of

interview p

articipants.

Six males and 4 female

completed Tough’s

Interview Schedule. All had previously completed the

SDLRS

(

LPA

) for this study a

were purposely chosen to be representative

of the volunteer community leaders. The

researcher based the se

lection on community leadership organization, ethnicity, age

and education. The majority of the participants (70%) were Caucasian; the others were

African American (10%), Asian or Pacific Islander (10%) and Hispanic (10%). There

were an equal number of par

ticipants between the ages of 46

55 (30%) and the ages

of 56

65 (30%). There were also an equal number of participants between the ages of

45 (20%) and 66

75 (20%). All were college gra

duates and 40% had some

graduate

education.

All described

their employment level as professional or

managerial.

Number of learning projects.

The 10 participants completed an average of

16.2 learning projects during the previous year that met Tough’s (1971) seven

hour

minimum. The median number of lea

rning projec

ts was 15. The time

spent on each

learning project averaged 123.1 hours, with a range from 10 hours to 2,000 hours.

Content of learning projects.

The subjects of this study participated in a

variety of learning projects during the 12 months before their i

nterviews. After the

interviews were completed, the researcher reviewed all the individual projects and

combined similar types of subject matter, identifying five main content categories:

employment

job

related, community organizations, personal interest

s, computer

technology, and current events. Aspects of these categories relating to community

leadership roles are discussed; the personal interest category is omitted in this paper.

SDL and Community Leaders

International Journal of Self

Directed Learning

Volume 7, Number 2, Fall 2010

Employment / Job

The largest number of learning projects that the

participants identified related to their primary employment. All the participants had

more than one learning project that was job

related. Although most included or began

with formal learning settings, almos

t all included some form of self

directed learning

as a part of the learning project. The topics were varied and reflected the participants’

different types of employment, but many of their work

related projects also enhanced

their community leadership ski

lls; for example, marketing, financial management,

survey methods, funding for public transportation, legal issues,

and tax issues

Community organizations

The second largest number of learning p

rojects

centered on the leaders’ work

community organizatio

ns. All participants identified

learning projects related to their work with community organizations. The subjects of

learning projects were varied and related to the needs of the individual community

leader and the organization. There were far fewer refer

ences to the inclusion of formal

learning segments in the learning projects related to community organizations as

compared to employment

job

related p

rojects. Sample quotes are included later in

this paper.

Computers /

Technology

All ten participants id

entified some type of computer

or technology

related learning project

that was self

directed. T

hese learning projects

focused on improving their skills, such as learning how to do PowerPoint

presentations, how to conduct Internet searches, and how to use n

ew technology.

Learning about computers and technology was viewed as a tool to support other

learning. As one leader commented, “

Internet research is probably one of the fastest

ways to educate yourself on a given subject and I found that ability, that ex

perience

very vital in the new job that I have had.”

Current events

Seventy percent of the participants reported that current events

were an ongoing learning project for them. They read the newspapers, read the news

online, watched the news reports and s

hows on TV, followed the stock market, and

attended meetings that involved local government issues. One participant said, “I’ve

always had an interest in current events and so I just make it part of my day. Current

events are just something that’s a part o

f life.”

Learning projects in relation to c

munity leadership r

oles

All

participants had voluntarily identified learning projects that related to their community

leadership roles before they were asked the final question, “Would you tell me about

any new learning required in your community leadership role?” When asked,

the

y all

referred to previously

identified learning projects that related to their

leadership of

community organizations. These were strongly represented in the

community

organization

computer/technology

, and

current events