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Chapter 1: Introduction to the Study
One consistently reported skill essential for college students is digital
competency, also known as digital literacy (Alexander et al., 2016; Gilster, 1997; Ray,
2018; Tyner, 2014). Digital literacy includes general capabilities individuals have for
living, learning, and working in a digital society while recognizing the changing nature of
digital technology and the developing potentials individuals may perceive as digital
citizens (Meyers et al., 2013; Ray, 2018). Because higher education institutions (HEIs)
deliver information, resources, and online learning tools, students must have the requisite
skills to enroll and register for courses and complete much of their coursework online
(Borokhovski et al., 2016; Miranda et al., 2018; Reddy et al., 2020). HEIs have both a
need and an obligation to aid students in becoming technologically literate. Gilster (1997)
introduced the concept of digital literacy in the late 1990s and defined it in academic
terms, understanding the groundbreaking impact of the internet on people’s lives. Gilster
described the digitally literate individual as having specific skills including assembling
knowledge, evaluating information, searching the internet, and navigating hypertext.
HEIs have assumed that college students are digitally competent. For example,
Martins et al. (2019) stated that because traditional college students—those under the age
of 25—have developed digital skills throughout their lives, college administrators have
presumed that all incoming college students are technologically ready for the demands of
higher education and do not need digital literacy support or training. However, this may
not be the case for the fastest growing population of college students, those learners
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known as “plus 50” or college students over the age of 50 years (Barr, 2016; Tsai et al.,
2017).
In this study, I explored the experiences of college students over 50 years of age
as they sought to acquire digital literacy skills within their college environments. The
social implications of this study are significant because the digitally informed citizen
should be an effective and attentive observer with the ability to assess digital resources
and activities that add value and identify disadvantages (Meyers et al., 2013; X. Wang et
al., 2021). Furthermore, this mindfulness of interrelated possibilities, socially and other
informal means, engenders a responsibility for keeping connections, a critical factor in
being digitally literate in the 21st century (Kvavik, 2005; Newman et al., 2018). Chapter
1 includes the background of the study, problem statement, purpose of the study, research
questions, theoretical frameworks, nature of the study, definitions, assumptions, scope
and delimitations, limitations, and significance.
Background
Colleges and universities try to retain students. Doing so is vital to an institution’s
reputation, funding, and viability (Pavlov & Katsamakas, 2020). However, attracting and
retaining older college students has been challenging for many universities (Twigg-
Flesner, 2018). For example, when students experience difficulties with technology,
which occurs most often within the demographic of over-50-years-of-age students, they
may be more likely to withdraw from a program of study or earn low grades (Darney &
Larwin, 2018). In addition, the independent nature of online enrollment, online
coursework, and online management of coursework-related activities, as well as the stress
3
associated with rigorous academic programs, can provide challenges for students over 50
as they try to form bonds with peers and faculty. Older students may also feel demeaned
due to their technological knowledge deficits (MacDonald, 2018). Technology can
overwhelm older students, especially if technology is not used in their personal and daily
lives. Furthermore, these students’ needs may not be apparent to administrators who can
and should be supporting them.
Digital literacy requires that individuals understand and work with digital data
within different structures using the internet to gain knowledge and improve skills (Betts
et al., 2019; Schreurs et al., 2017). However, there is evidence that older college students
have low confidence in their technological abilities (Henson, 2015; Sultan & Kanwal,
2017). Betts et al. (2019) discovered that older adults favored personalized learning on a
one-to-one basis for learning technology. Results also indicated that some older adults
found group sessions a barrier to engaging with digital technology. Without these
technical capabilities, older learners may not fully access or participate in technologically
driven higher education environments compared to their younger peers (Darney &
Larwin, 2018).
Adult students have become a prominent presence in colleges during recent
decades and prefer specific ways of learning. These adults’ effective learning strategies
include inquiry-based learning, active learning, and self-initiated ways to persevere and
persist in college success (Hennessy et al., 2021). An operational social environment of
inquiry is beneficial for older adults learning technology (Antonucci et al., 2017; Chopik,
2016). Institutions may have underestimated the abilities and needs of the older learner.
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Schreurs et al. (2017) found that older adults recognize differences in their knowledge of
technology compared to younger generations. These researchers discovered that
involvement and experience with technology are vital for older students. Such connection
and immersion aid in reducing the stress and anxiety of technology used for these
students and reinforce the potential relevance of technology, which promotes self-
determination and self-efficacy related to technology.
Not all research confirmed Prensky’s (2001a, 2001b) digital divide between
digital natives (using technology in childhood) and digital immigrants (adopting
technology in adulthood). Friemel (2016), agreeing with Schreurs et al. (2017), argued
that the digital divide between the digital natives and the immigrants does not include
over-50 college students because it does not differentiate among those who are 50–59
years, those who are 60–69 years, and those who are over 70. Friemel found that the
likelihood of internet use decreases by 8% with each added year of age. Adults 50 years
and older may experience inequalities in technology abilities compared to younger
people, suggesting that Prensky’s notion of a digital divide persists.
When students lack social connections with faculty and peers, they risk
withdrawing from their studies. Antonucci et al. (2017) showed that links with and
through technology are essential for forming relationships within social settings; older
students may be the most at risk for failing at this endeavor (Vaportzis et al., 2017).
Through technology, new methods to maintain social relations within families and other
societal layers, including academia, represent a basis for developing a sense of
community. Technological developments expand communication options for older adults
5
with mobility limitations (Antonucci et al., 2017). Older students’ involvement with
technology in higher education may reflect how they learn about technology outside of
education and the support needed to become proficient and confident with technological
devices (Leu et al., 2017).
Older college students tend to acquire technological skills differently than
younger students who have used technology throughout their K–20 education. Tsai et al.
(2017) examined how older adults learned to use a specific technology such as an iPad.
These researchers found that the ability to use software or operate a digital device
included an assortment of cognitive, motor, sociological, and emotional skills, which
adult learners needed to develop to function in digital environments. These researchers
also concluded that both the senior technology acceptance and adoption model (see
Renaud & Van Biljon, 2008) and the social cognitive theory (see Bandura, 1977, 1994)
provided insight into older adults’ technology learning processes.
Active learning focuses on developing learners’ skills rather than merely
transmitting information (Lipphardt et al., 2017). Lipphardt et al. discovered that older
adults desired experiential techniques for learning technologies with a hands-on approach
in which they manipulated equipment or other materials. Older adults can learn within a
social context and through experimentation that college faculty may not offer in a typical
course.
There is evidence that students over 50 value digital access for social connections.
They use the internet to maintain friendships and hold casual day-to-day conversations
using digital tools (Bixter et al., 2019) such as email and Skype (Quan-Haase et al.,
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2017). However, supplementing those accessible digital abilities, rather than forming new
ones outside older students’ existing competencies, may be transferred to the technology
used in college. Quan-Haase et al. (2017) argued that for these students to learn how to
become skilled at certain technologies, they require a form of social support, thereby
strengthening relationships among student peers young and old. There was a gap in the
literature concerning personal perspectives of college students over the age of 50
regarding acquiring technology skills (see Aikens-Alston, 2016). In this study, I
identified supports and types of learning that over-50 college students who attended
campus-based and online 4-year institutions required for their digital literacy
development to keep pace with developments within their learning environments.
Problem Statement
Even though college students over 50 years of age may have made some recent
gains in technology use (Darney & Larwin, 2018), this population group is the least
expected to gain momentum with ongoing technological shifts and advancements (Tsai et
al., 2017; Vassilakopoulo & Hustad, 2021). A substantial barrier to older adult learners
using the internet has been a wide-ranging lack of digital literacy (Blažič et al., 2020;
Jacobson et al., 2017). College students over 50 years of age may not have the digital
skills or habits to use the technologies (Neves et al., 2018) required of them while in
college, thereby creating a disadvantage within their social, educational, and career
development (Jacobson et al., 2017). In addition, although there was some evidence that
students over 50 have a range of technical abilities (Friemel, 2016; Tsai et al., 2017),
there was a gap in the research regarding the needs of these learners, how they learn
7
digital skills, and how best to support them in acquiring these skills (see Jacobson et al.,
2017; Schreurs et al., 2017).
For college students over the age of 50 born before their younger peers who are
digital natives (Prensky, 2001a, 2001b; Ray, 2018), learning new technologies and
computer skills is a requirement to be successful in the digital classroom (Schreurs et al.,
2017). Prensky (2001a, 2001b) described digital natives as individuals who have used
digital technologies since birth and who have an advantage over digital immigrants,
defined as those introduced to digital technologies in adulthood who have had to learn
about and how to use digital tools in their adult years (Ray, 2018). Digitally literate
people use technological developments to enhance their educations, social lives, and
careers (Y. Wang et al., 2015); however, digital immigrants may be disadvantaged with
technologies that support learning.
College students over 50 years of age are at a greater risk than their younger
colleagues for not overcoming academic ordeals and dilemmas (Zhou & Salvendy, 2017).
Over-50 college students have reported experiencing difficulties. They struggle with
managing their online student accounts, paying their tuition bills, being confident about
proper research via the online library, and many other challenges related to being
technologically aware (Montalto et al., 2019). This population may be technologically
illiterate (Barr, 2016). College is a stressful and challenging environment, and when older
students feel daunted or intimidated by digital tools, they may experience reduced
academic effort and effectiveness.
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Purpose of the Study
The purpose of this basic qualitative study was to explore and better understand
the supports and types of learning required for digital literacy development for students
over 50 years of age who attended 4-year institutions. For this study, supports were
institutional or noninstitutional services or human interactions that improve an
individual’s digital literacy and comfort level using technology. Types of learning were
based on Mezirow’s (2009) theory of transformative learning using (a) instrumental
learning, which is technical and involves problem-solving, and (b) communicative
learning, which is observational and interpretive and involves self-reflection. The digital
literacy (Gilster, 1997; Ray, 2018) tenets of comprehension, interdependence, social
factors, and duration provided a lens to examine over-50 college students’ use of and
facility with technology and learning.
Research Questions
RQ1: How do college students over 50 years of age describe their learning as they
develop digital literacy skills?
RQ2: What types of support do college students over 50 years of age report most
helpful in developing digital literacy?
Conceptual Framework
The conceptual framework for this study included two models: digital literacy and
Mezirow’s (2009) transformative learning model. I used each framework to understand
better how students over the age of 50 acquire digital literacy and what best supports their
skill acquisition. The rapid development of digital technologies in the 21st century
9
requires older individuals to use various cognitive skills to perform and solve different
digital environments. For this study, I referred to skills such as digital literacy, which is a
mindset enabling users to act in digital environments intuitively and to access a wide
range of knowledge embedded in these environments (see Gilster, 1997; Inoue et al.,
1997; Lanham, 1995; Pool, 1997; Ray, 2018; Tapscott, 2009).
Digital literacy consists of four tenets: comprehension, interdependence, social
factors, and duration (Gilster, 1997; Osterman, 2012; Ray, 2018). For this study, I used
two of the four tenets. First, I used the tenet of interdependence to explain the help
needed by over-50 college students to succeed academically. Interdependence is how one
media form connects with another. Individuals do not create digital materials in isolation
or with the intent that no one will see them; therefore, publishing to multiple platforms is
easier to accomplish (Perez, 2018; Rashidian et al., 2018). Owing to the profusion of
digital material, such media forms not only coexist but also supplement one another and
require digital literacy (Becker, 2018). Over-50 college students may not have experience
with technology interdependence, which is inherent to learning through current
technologies. Second, interdependence is a tension between individuals’ yearning for
independence and their desiring support from others (Orehek & Kruglanski, 2018).
Humans crave personal autonomy, but they also yearn for a connection to others that may
constrain a certain amount of individual freedom (Orehek & Kruglanski, 2018).
Interdependence in education for over-50 college students may create a collaborative
learning environment in which individuals work toward shared goals.
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Social factors were the second tenet of digital literacy used to inform this study.
Social factors indicate why an individual may need specific support and which social
conditions contribute to improved digital literacy (Gilster, 1997; Ray, 2018). The key to
the learning process for college students over 50 “is support from environmental factors,
which can include the support of family, friends, or those important to the individual’s
life” (Tsai et al., 2017, p. 34). In addition, social factors may impact an individual’s
acceptance of or adoption of digital tools and encouragement to access the internet. These
factors are relevant to older students because they may not be digitally literate and may
require considerable technical and social support (Meyers et al., 2013).
The second theory that constituted the conceptual framework for this study was
transformative learning conceived by Mezirow (2009). Mezirow (1997) emphasized how
adult learners understand and translate their experiences, which is vital to making sense
of the learning process. Transformative learning involves two kinds of learning: (a)
instrumental learning, which focuses on learning through task-oriented problem solving,
and (b) communicative learning, which involves how individuals communicate their
feelings and desires (Howie & Bagnall, 2013; Mezirow, 2009). In the current study, I
explored what college students over 50 reported regarding developing digital literacy
acquisition using these types of learning to become digitally proficient. Transformative
learning is essential for over-50 college students, particularly those returning to college to
further their career opportunities (Hoggan, 2016). For Mezirow (2009), the desired
transformation outcome is an individual’s development of autonomy through a greater
sense of self-sufficiency and independence. In addition, some over-50 college students
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are facing, possibly for the first time, a technological universe with which they are
unacquainted, generating what Mezirow (1997) referred to as a change in an individual’s
frame of reference, thereby triggering new learning to occur. Combined, these two
theories provided insight into the processes used by over-50 college students and the
supports that aid them. I provide a more detailed explanation and analysis of digital
literacy and transformative learning in Chapter 2.
Nature of the Study
This basic qualitative study was conducted to better understand older students’
acquisition of digital literacy skills and potential supports received or needed, and types
of learning that best serve older students’ needs in developing digital literacy skills.
Merriam and Tisdell (2016) asserted that qualitative methodology aids the researcher in
gathering evidence. The investigator scrutinizes the experiences of study participants,
providing an understanding of each person’s distinct beliefs (Merriam & Tisdell, 2016).
Through their stories, the participants describe their views of reality, enabling the
researcher to comprehend their perceptions and experiences (Teherani et al., 2015).
Because little was known about individual perspectives of college students over 50
related to the use of technology within higher education, I used a qualitative approach.
The phenomenon explored in the study was the digital literacy acquisition of
college students over 50 years of age. This population tends to lack digital skills or may
not have support to use the technologies required of them while in college, creating a
disadvantage for those individuals (Jacobson et al., 2017). Digital literacy is a capability
in which an individual comprehends and processes information from various sources
12
presented via digital devices such as tablets, phones, or computers (Ray, 2018;
Techataweewan & Prasertsin, 2017). Mezirow’s transformative learning can be used to
explain learning for other adults because it emphasizes learning through task-oriented
problem solving, which is learning appropriate for older students (Knowles, 1984; New
England Institute of Technology, 2021; E. W. Taylor & Laros, 2014). The key to these
students’ learning process includes social factors that may increase their receptiveness to
and engagement of digital tools (Bandura, 1977, 1994).
A basic qualitative methodology allows a researcher to inquire about a
population’s interpretation of experiences, how they made sense of these, and the
meaning they gave to their experiences (Merriam, 2009). In my endeavor to understand
the experience of acquiring digital literacy, this approach was a good fit. According to
Merriam and Tisdell (2016), qualitative researchers make use of “qualitative data
collection methods, such as interviews, focus groups, observations, and analysis of
documents or artifacts” (pp. 52–53). I obtained data through one-to-one interviews with
12 participants and two focus groups with a small size to allow for in-depth conversation
(see Lauckner et al., 2015).
The research process began with recruiting undergraduate college students over
50 years of age through the Walden University participant pool and social media. Once I
secured institutional review board (IRB) approval and consent from participants, I
collected data through individual interviews and two focus group sessions. Due to the
restrictions of the COVID-19 pandemic, I scheduled digital calls for personal interviews
for approximately 30–45 minutes using open-ended questions (see Appendix). After
13
transcription and data analysis of the interviews, I determined topics for the online focus
group sessions. Next, I digitally recorded two 1-hour focus group sessions, each with
three participants (a total of six individuals) from those who had taken part in the
individual interviews. Data analysis involved applying precodes, identifying emergent
codes, and determining themes (see Merriam & Tisdell, 2016; Saldaña, 2016) related to
participants’ descriptions of supports they expressed needing while developing digital
literacy. Throughout this process, I identified patterns by looking for parallels, variances,
and arrangements within the data (see Saldaña, 2016).
Definitions
Associative learning: A theory based on a learner’s error reduction from
associations among representations of environmental stimuli (i.e., cues) that signal the
occurrence of outcome events. Efficient acquisition of these associations requires
discovering and focusing on the most relevant stimuli while ignoring distracting or
irrelevant stimuli (Mutter et al., 2019).
Campus-based institution: Campus buildings and grounds are employed as
teaching resources, and the campus is related to education. Learners physically commute
to or live on campus to receive instruction (Boulton et al., 2019).
Communicative learning: This type of learning occurs when at least two people
are trying to figure out what an interpretation means or why they believe what they
believe. The goal of communicative learning is to attain an agreement (Mezirow, 1997).
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Digital literacy: Instruction delivered via computers, and the ability to absorb and
utilize information in many different formats from a variety of sources (Techataweewan
& Prasertsin, 2017).
Digital immigrants: A group of people introduced to digital technologies in
adulthood and who have learned about and used digital tools after their youth (Prensky,
2001b).
Digital natives: According to Prensky (2001a), people who have been exposed to
digital technologies since birth and who have an advantage over those who have adopted
technology in adulthood.
Instrumental learning: Learning that is based on behaviors or responses that are
impacted (or not) by the current value of the result connected with them and can be either
goal-directed acts or habits (Trask et al., 2017).
Mindful learning: A type of learning that involves a relaxed state of mind in
which people are actively engaged in the present, aware of new things, and sensitive to
context as it helps improve attention, cognitive flexibility, problem solving, emotion, and
working memory (Xiao et al., 2017).
mLearning: Mobile learning that combines mobile communications technologies
with eLearning, which is any form of electronically delivered learning material
emphasizing internet-based technologies through mobile devices (Arvanitis, 2019).
Nontraditional students: This population includes students over 25 years of age
who are enrolled in school part-time and are generally financially independent. These
15
students are full-time employed while enrolled in school, and many of them have
dependents, including young children (MacDonald, 2018).
Older learner: Those over 50 years of age who have chosen to return to the
classroom in pursuit of additional degrees or to study topics of interest (Barr, 2016; Parks
et al., 2013). The term includes other terms that fit this description: over-50 student, plus
50, and adult learner.
Online institution: A degree-offering college or university delivering courses,
support services and operation, and activities solely through internet-based learning
management system and other systems (Dumford & Miller, 2018).
Online student: A student who does not attend classes on campus and completes
their studies through the internet (Stone & O’Shea, 2019).
Assumptions
Assumptions are facts or ideas that individuals perceive or believe to be accurate
but are not verifiable (Nkwake, 2013). Assumptions are necessary for research because
they allow the researcher to facilitate and conduct the study (Simon & Goes, 2013). One
assumption in the current study was that participants would sincerely and openly take
part in the interview sessions and the focus groups. A second assumption was that the
individuals who participated in the interviews and the focus groups communicated openly
and honestly without ulterior motives.
Scope and Delimitations
The purpose of the study was to explore how college students over 50 acquire
digital literacy and what supports they reported as helpful in this endeavor. The problem
16
was that these students might not have had the digital skills required of them. At the same
time, college challenges these older students to navigate various aspects of college
studies, including managing their online student accounts, paying their tuition bills, being
confident about doing proper research via the online library, and many other challenges
related to being technologically aware; older students might be considered
technologically illiterate (Barr, 2016; Jacobson et al., 2017).
Delimitations of a study result from intentional limitations in the scope of the
research and arise via conscious decisions made during the development of the study plan
(Simon & Goes, 2013). Delimiting factors include the option of goals, the research
questions, the variables of interest, and the theoretical perspectives adopted. In the
current study, the first delimitation was choosing the problem. The purpose statement
explained the study’s intent, set out proposed accomplishments, and included a clear
explanation of what the study would not cover (Simon & Goes, 2013).
Delimitations imply limitations on the research design that the researcher has
deliberately imposed (Simon & Goes, 2013). Delimitations of the current study restricted
the transferability of findings to other populations such as younger students. I had
planned to select five students from online institutions who were taking courses online.
However, because of the COVID-19 pandemic, all participants were taking courses
online. I did not include students under the age of 50 even though nontraditional students
may be anyone over the age of 25.
I did not include faculty members as study participants. Nonetheless, faculty
members play a vital role as change agents in creating supportive learning environments
17
for adult learners (Cross, 1994; Qalehsari et al., 2017). Faculty members may have
expanded this study by incorporating theory and research used within their learning
environments. In addition, faculty often advocate for adult-oriented programs and
services on their campuses (Qalehsari et al., 2017). My exclusion of faculty restricted a
broader depiction of the problem. Because of the limited population, transferability to
other settings was also limited because different institutions vary in demographics and
support offered to students.
Limitations
Limitations are potential weaknesses in a study beyond a researcher’s control and
often emerge from research method and design choices (Cunha & Miller, 2014; Simon &
Goes, 2013). One limitation in the current study was using two focus groups, which
limited the outcome in addressing the research questions due to the small sample size.
Some participants chose not to attend the focus group session after the individual
interviews. However, I obtained rich information through the discussions, and the focus
groups clarified and elaborated on interview data.
There might have been a limitation in my understanding of the potential
participants within a group setting or knowing how to approach them best if they
appeared reluctant to participate in a focus group. Nevertheless, I treated all participants
with respect and consideration, listened to their opinions with full attention, and provided
as much time as needed. Another limitation was that within a qualitative study such as
this one, personal bias could have influenced the intended outcome of the study. I
addressed any bias by using member checking, also known as participant or respondent
18
validation, to identify areas that I may have interpreted inaccurately (see Birt et al.,
2016).
Significance
Due to their lack of digital literacy, college students over 50 may flounder in their
coursework, potentially impeding their academic progress. Because this demographic is
an increasing percentage of college students (Schreurs et al., 2017; Tsai et al., 2017),
HEIs should consider this population’s needs as they seek to acquire digital literacy.
Adults achieve digital literacy through practice, which continues beyond an individual’s
first exposure to technology (Ray, 2018). Older adult learners often need training because
they are reluctant to try current or innovative technologies (Techataweewan & Prasertsin,
2017).
Jacobson et al. (2017) identified essential ranges of abilities and technology
preferences of older adult learners, indicating a high degree of inconsistent experiences
within this generational group. Although the number of adult learners embracing
technology continues to grow, there still seems to be a generational gap (Friemel, 2016).
For older adults, the benefit of digital literacy skills is critical because their adoption may
have tangible benefits to their lives and their careers (Friemel, 2016). Results of this
study may help HEIs understand the types of support needed by older adult learners and
the types of learning they apply as they use technology tools to gain academic success,
which may promote opportunity for all generations so that everyone can be a successful
digital learner. In addition, HEIs may use the findings to offer new resources and policies
19
regarding expectations about digital literacy and keep abreast of current trends (see Neves
et al., 2018; Vassilakopoulo & Hustad, 2021).
Summary
This study addressed the problem that college students over 50 years of age
typically do not have the digital skills or habits for use with technologies required of
them while in college. The purpose of this qualitative study was to identify and
understand specific types of learning and needed supports necessary for digital literacy
development for these students. Chapter 1 presented the background of the study,
including a discussion of several learning approaches appropriate for students over 50,
focusing on Mezirow’s (1991, 1996, 2009) transformative learning theory. Using a basic
qualitative design, I explored the need for support and the type of training required for the
over-50 population.
Chapter 2 presents the conceptual frameworks I used for this study, an
explanation of the literature search process, and a review of the research literature
relating to the study problem, purpose, and research questions. The literature review
details the background of the problem and gaps in the current literature. This study’s
primary goal was to explore the over-50 college student’s digital literacy and needed
supports for using technology required in higher education.
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Chapter 2: Literature Review
New technological learning and teaching tools in HEIs have produced unique
teaching and learning strategies requiring students of all ages to adapt and acquire skills
that correspond to this changing landscape. These skills are known as digital literacy, a
capability in which an individual comprehends and processes information from various
sources presented via digital devices such as tablets, phones, or computers (Ray, 2018;
Techataweewan & Prasertsin, 2017). One of the groups challenged with embracing
digital literacy is learners over the age of 50, who may face new technology with a
combination of fear and resistance. However, such technologies have also been met with
acceptance and approval of older adults in certain scenarios such as when they are
curious about societal progress or wish to be digitally conversant and discover a variety
of new technologies (Costa et al., 2019; Vaportzis et al., 2017). The purpose of this basic
qualitative study was to identify and better understand the supports for these older
learners and whether transformative learning (see Mezirow, 1991) occurs in the
development of digital literacy for college students over 50. Transformative learning
emphasizes task-oriented problem solving, which is appropriate for older students.
The population of college students over 50 who may be confident with routine
uses of technology such as phones and internet access may not understand digital
concepts related to skills needed to attain a college degree (Tsai et al., 2017). For older
adults, a significant obstacle to discovering modes of technology has been an absence of
their digital literacy (Jacobson et al., 2017). The problem is that these students may lack
the digital skills or habits for use with technologies required of them while in college,
21
thereby creating a disadvantage in their college and academic ambitions (Jacobson et al.,
2017).
There is evidence that some older students possess a range of technical abilities
(Hunsaker et al., 2019; Tsai et al., 2017). However, there is a gap in the research about
the needs of these learners, how they best learn digital skills, and how to support them in
acquiring digital acuity (Jacobson et al., 2017; Schreurs et al., 2017). Research indicated
that a perspective transformation explains the process of how adults learn as they revise
their meaning structures (Hoggan et al., 2017). Meaning designs include meaning
schemes and perspectives (Mezirow, 1991). Meaning schemes (the smaller components)
are “made up of specific knowledge, beliefs, value judgments, and feelings that constitute
interpretations of experience” (Mezirow, 1991, pp. 5–6). Higher education administrators
cannot support this population’s digital literacy acquisition if they do not understand how
older students’ meaning structures are unique. Chapter 2 includes a description of the
literature search strategy and an overview of the conceptual frameworks that informed
this study. I then provide a detailed examination and analysis of empirical research
related to the research question, followed by a summary of the chapter findings.
Literature Search Strategy
This literature review process involved a comprehensive examination of literature,
research, and empirical data for this study addressing adult learning theory and digital
awareness in over-50 individuals. I began the search with scholarly and peer-reviewed
articles and books using multiple databases including EBSCO, ProQuest, PsychInfo, and
PubMed. I also searched Google Scholar and books related to the subject matter. I
22
focused on literature published between 2010 and 2018 except for seminal works on the
topic from earlier dates. Keywords included digital literacy, transformative learning,
communicative learning, digital natives, digital immigrants, technological skills,
technology in higher education, instrumental learning, older adult learners, over 50
college students, and non-traditional students. I excluded any literature on adolescents
with technological learning challenges, focusing on the older adult population.
Conceptual Frameworks
I recruited college students over 50 years of age who lacked digital skills or did
not have support to use the technologies required of them while in college, creating a
disadvantage for those individuals (see Jacobson et al., 2017). I used two conceptual
frameworks: transformative learning, which relates to how adults learn (Mezirow, 1991),
and digital literacy, in which individuals demonstrate digital skills and competence
(Gilster, 1997; Prensky, 2001a, 2001b; Ray, 2018).
Transformative Learning Theory
Approaches to adult learning have served as valuable lenses for research on the
older learner; these frameworks show practices across various adult learning contexts
including the college classroom. I selected Mezirow’s transformative learning theory
because it emphasizes learning through task-oriented problem solving, a type of learning
appropriate for older students (M. S. Knowles, 1984). The key to these students’ learning
process includes social factors that may influence a favorable reception and engagement
of digital tools (Bandura, 1977, 1994). Older students encounter a technologically
23
immersive environment requiring a change in their frames of reference, generating new
learning (Mezirow, 1997).
Mezirow’s transformative learning theory emphasizes learning through task-
oriented problem solving, a type of learning appropriate for the older student (M. S.
Knowles, 1984). Transformative learning is one of the most prominent theories in adult
learning research (Mezirow, 2000). Mezirow (1997) described transformative learning as
a fundamental transformation of the adults’ core frames of reference, often in response to
disorienting dilemmas that challenge an individual’s existing way of thinking about the
world, prompting them to reflect critically on previously held assumptions. For example,
an adult college student in a history course may have had preconceived ideas about U.S.
history. However, they may discover a changed worldview through new course learning
and experience a personal transformation during the learning process. Much of the
research on transformative learning has focused on adults in higher education and certain
life situations of older adults. Some research has proposed that educators can help
stimulate transformative learning by using teaching methods that foster critical reflection
(Chukwuedo et al.,
2021).
Transformative learning offers a framework that is also distinctively adult
oriented and grounded in human communication, as “learning is understood as the
process of using a prior interpretation to construe a new or revised interpretation of the
meaning of one’s experience to guide future action” (Mezirow, 1996, p. 162). Mezirow
(1991) explained how adult learners make sense of the meaning of their experiences and
how social and other structural supports influence the way adults interpret such
24
experiences. The dynamics involved in modifying meanings include changes learners
must make when they find themselves in a disruptive or unsettling situation, such as
when instructors insist older adult students use technology when they are not prepared.
For example, the adult online learner encounters many new digital tasks, new digital
interfaces, and new ways of approaching learning, thereby disrupting preconceived
notions of how one goes about learning. Nonetheless, students over 50 benefited from
this learning as they begin to change their customary frames of reference, mostly in
response to a confusing or complex challenge. They confront their prior ways of thinking
about the world, prompting them to reflect critically on previously held assumptions
(Mezirow, 1996, 2009; Ross-Gordon, 2018).
Transformative learning theory explains how adults learn through such moments
of imbalance and are reflective of digital learning. The approach is rooted in the belief
that learning occurs when learners assign new meaning to earlier experiences (Mezirow,
1990). Learners reinterpret an existing meaning when they begin to see it in a new light.
There are three stages of learning in transformative learning. The first stage
involves a revelation that an individual held on to incorrect beliefs or did not know what
they should know is frequently a motivation for digging deeper and unearthing
information or reviewing their thinking habits. Recognizing that one’s beliefs are
inaccurate is a dilemma that can be serious. When applied to learning, an instructor must
probe what students do not know in order to pique their interest in what they need to
learn (Mezirow 1997).
25
The second stage establishes personal relevance. This is the perspective or
response to the question of “what is in it for me?” that motivates people to study in
personal, professional, or social settings. Instructors should spark students’ interest early
on and repeat the value of the content frequently to keep students involved. When adults
can see the outcomes of their efforts, they are more driven to learn (Mezirow, 1997).
The third stage involves critical thinking, which is relevant for older learners who
are intelligent, logical individuals. These students should make every attempt at
introspection (foundation assessment) to motivate them to reconsider their perceptions
and opinions. These students will be more inclined to accept and absorb the lesson if an
instructor allows them to sort through their emotions and experiences and understand on
their own and determine what they need to reject or modify (Mezirow, 1991).
Research Using Transformative Learning Theory
Mezirow’s (1997) theory of transformative learning is one of the foundations for
adult education and describes alterations during the learning process. This theory also
provides direction for personal and social development. Transformative learning allows
individuals to reflect on their knowledge and beliefs, guiding learners in contemplating
their values (Mezirow, 2000). Lee and Brett (2015) used Mezirow’s transformative
learning of older adults to illustrate their transition from learner to volunteer.
Educators are expected to adapt to new instructional environments and apply new
educational technologies. However, this process can be lengthy and can result in a shift in
their viewpoint. Lee and Brett (2015) convened a study with 44 in-service teachers
pursuing graduate degrees. To support teachers’ transformative learning, the researchers
26
created and presented a discussion-based online course. The nature of effective online
teacher-to-teacher talks was investigated in this qualitative case study of teachers’
perspective transformation. The study was based on a theoretical framework that
incorporated Mezirow’s transformative learning model with Bakhtin’s dialogism.
Transformative learning applies to adults learning new technologies and new
modes of thinking. Mezirow (2009) described this process as one in which individuals
“transform problematic frames of reference (mindsets, habits of mind, meaning
perspectives) – sets of assumption and expectation – to make them more inclusive,
discriminating, open, reflective and emotionally able to change” (p. 92). Ilomaki and
Lakkala (2018) used a qualitative approach to examine older adult staff in two public
schools as they experienced a change in the curriculum and leadership involving
technology. Although transformation did not come without significant challenges for
these groups of school personnel and without mindful awareness of the necessity to
change, a change was successful because it was more intrinsic than external factors might
enable. Without identifying the need for change and determination to persevere,
transformation might not have occurred. Findings indicated that a level of tenacity and
resolve in exploring solutions to challenges while focusing on the essential modification
in meaning structure ensures that students of all ages can successfully transform.
Nerstrom (2017) used a narrative inquiry to analyze the personal stories of six
adult educators who had self-reported prior transformative learning experiences as they
went through a graduate program. Through the participant stories, most of whom were
over the age of 50, Nerstrom found that participants’ self-confidence increased, a critical
27
component in creating independent thinkers. Individuals with low self-confidence are less
likely to be open to new concepts. Mezirow (2000) described self-confidence as a critical
factor in fostering transformative learning that is strongly associated with openness to
varied views. Mezirow (1994) stated that “building competence and self-confidence in
new roles and relationships” (p. 224) is foundational to completing the stages of
transformation. Openness to new ideas enables older individuals to reflect critically on
previously accepted assumptions.
Mezirow’s (1978) transformative learning components include experience, critical
reflection, and rational discourse. A 2020 study by King examined these aspects as they
sought to understand the connections between learning and social movement, particularly
within Lebanon. Using semi-structured interviews and a focus group discussion with
learner-activists and adult educators, King discovered that engagement in a social
movement challenged and changed learner-activists’ understanding of educational status
within their respective communities, leading to transformative action addressing
problems identified. An emerging element of this study was the ability of an individual to
not only think critically but to reflect critically as well, mirroring one of Mezirow’s tenets
to make a “critical assessment of assumptions” (Mezirow, 1994, p. 224). Thus,
transformative learning allows individuals to reconfigure their existing perspectives and
realities, demonstrating that they are genuinely qualified to become active agents in
assessing and transforming outdated attitudes.
Transformative learning reflects a vision of how adults learn as they connect
various personality traits entrenched from their early educational experiences and
28
personal resources they have developed throughout their lives. Older students’
transformative learning focuses on engaging personal factors of critical reflection,
creativity, and critical thinking (Mezirow, 1994). Individuals’ learning depends on their
basic life concepts, values, and responses, encompassing their life experiences. Still, the
process of learning is also based on a person’s ability for critical thinking, self-direction,
and curiosity
(Chukwuedo et al.,
2021). Strange and Gibson (2017) examined educational
practices of students enrolled in study abroad programs through a large south-eastern US
university. to determine the efficacy of transformative learning for lifelong learners of
vocational studies. These characteristics are intrinsic to Mezirow’s transformative
learning process. As lifelong learners, the adults studied in Strange and Gibson (2017),
research possessed qualities of an ongoing and self-motivated pursuit of knowledge for
personal or professional reasons.
Mezirow’s theory requires adult learners to be aware of several learning
components including critical reflection, metacognitive reasoning, and the questioning of
assumptions and beliefs (Hughes & Yarbrough, 2022; Tsimane & Downing, 2020).
Personal transformation through learning is not something that happens to students.
Instead, it is a dynamic process in which those individuals are actively engaged,
including challenging technology and digital literacy topics.
Digital Literacy
Digital literacy is a capability in which an individual comprehends and processes
information from various sources presented via digital devices, such as tablets, phones, or
computers (Ray, 2018; Techataweewan & Prasertsin, 2017). Digital literacy consists of
29
several tenets, two of which are a priority in this study. First, interdependence is how one
media form connects with another and exists as a trait of human nature wherein tension
exists between an individual’s yearning for independence and their desiring
interdependence with others. Humans crave personal autonomy and desire connection to
others which may constrain individual freedom (Cook, 2013; McCarthy-Jones, 2019).
Thus, interdependence within learning, specifically for older students, can create a
collaborative learning environment wherein individuals work together toward a shared
goal. Second, interdependence explains the help and support needed by college students
over 50 to succeed academically.
The second tenet of digital literacy that informed this study is the social factor,
indicating why an older student may need specific support and which social conditions
contribute to improved digital literacy (Gilster, 1997; Ray, 2018). The key to the learning
process for students over 50 “is support from environmental factors, which can include
the support of family, friends, or those important to the individual’s life” (Tsai et al.,
2017, p. 34). Social factors may influence an older student’s adoption of digital tools and
encouragement to access the Internet. These factors are particularly relevant to the over-
50 learners because these individuals may not be digitally literate and may need much
technical support and social support (Meyers et al., 2013).
The adoption of digital literacy skills to improve the quality of college learning is
a critical issue for the HEI digital learning environment. Digital content and learning
objects are widespread among students to facilitate self-directed learning (Rashid &
Asghar, 2016). However, practical and effective learning with digital literacy requires
30
students to be eager to acquire knowledge, think critically, and apply existing knowledge
to novel knowledge or innovation (McDougall et al., 2018). Rashid and Asghar (2016)
argued that technology-rich learning environments could provide students with more
significant opportunities to be self-directed in their learning. Such an environment calls
for the students to know the proper content selection and manage appropriate usage of the
information.
Research on Digital Literacy and Older Adults
Older adults have not grown up with digital tools and may not rely on these tools
in the same ways as the younger generations. Thus, they may or may not understand how
to use technology to learn, even if they are comfortable using technology for
communication or entertainment. Early research in this area by Barnard et al. (2013)
revealed two older adult technology literacy components. They conducted two qualitative
case studies to explore two models of learning technology: acceptance and rejection. One
study viewed the older adults’ approval or rejection of digital information from a learning
perspective, and the second assessed older learners from a system and user perspective.
The main finding of this study showed that the mindset and attitude of the more senior
user determined acceptance or rejection of the technology introduced. Thus, as innovative
technologies emerge, a new generation of older adults will likely face new challenges.
One of those challenges within the digital environment is a concern about privacy.
Elueze and Quan-Haase (2018) performed a study interviewing 40 adults aged 65 and
older. Findings indicated that older adults held a wide variety of developmental privacy
worries described by five characteristics: fundamentalist, intense pragmatist, relaxed
31
pragmatist, marginally concerned, and cynical expert. For example, a fundamentalist
would avoid social media sites and not engage in services, such as online banking. On the
other end of the spectrum, the cynical expert doubted that nothing could protect
individuals’ privacy against large corporations. The three other categories fell somewhere
in between these two. This continuum of privacy concerns expands an existing scale
created by Westin (1967) related to the Internet and digital privacy rights. Although there
were various and wide-ranging privacy issues in Elueze and Quan-Hasse’s study, their
findings about older adults’ concerns about privacy offers a suitable strategy to overcome
hurdles and make progress towards acceptance while trying to engage older adults with
the digital environment.
Careful adult digital literacy research categorizes age group differences because
some researchers define older adults as those over age 50, while others designate older
adults over age 90. Hargittai and Dobransky (2017) examined the digital literacy of older
adults, using a U.S. national survey to determine the online skills and behavior of older
Americans. The findings indicated a broad scope of internet skills and applications.
Individuals with more education and money had more developed digital and internet
skills. Furthermore, those with a higher socioeconomic status were able to use the
internet for a variety of purposes, including receiving news, completing banking
transactions, and socializing with friends and family. These researchers further divided
the demographic of older adults into several age categories: aged 55 to 65, aged 65 to 79,
and those over the age of 80. When using the term college students over 50, there is a
range of potential age brackets as seen in categories defined by the researchers. Hargittai
32
and Dobransky reported distinct differences in digital use. They needed support as those
aged 55–64 described the highest level of skills while those in the 80–97 category
expressed the lowest-level skills. Nonetheless, researchers have not produced detailed
information about Internet use and skills paired with one or more of the specific age
ranges listed above for those aged 65 to 79.
The National Center for Education Statistics (NCES) is a federal organization that
collects, analyzes, and reports data related to education in the United States and other
nations. In research conducted for NCES, Rampey et al. (2016) noted that literacy in the
21st century requires critical thinking and problem-solving skills within technology-rich
environments, using digital communication tools and networks to acquire and evaluate
information, communicating with others, and performing practical tasks (Schreurs et al.,
2017; Tsai et al., 2017).
Research has shown the importance of planning specific models or strategies to
allow the older adult population to acquire and enrich their digital competencies more
easily (Di Giacomo et al., 2018; Martínez-Alcalá et al., 2018). The following analysis of
the literature identified some of the hurdles that older adults encounter while using
technology that they find hard to use. Understanding common barriers that older adults
experience when adopting new technologies can provide insight into how better to
support them and how to improve digital product design and development. As a result,
technology tools can be simpler to use and master, making them suitable for people of all
ages.
33
Literature Review
I examined critical concepts by synthesizing current literature that provides
extensive knowledge about the older learner and digital technology. Adopting technology
for this population of older adults is contingent upon an individual’s attitude, ability, and
approach to technology use in learning, both informally and within higher education
(Chiu & Liu, 2017). Thus, there is a need for learners of all ages, particularly older
adults, to prepare themselves for a future immersed in technology and technology use in
education (Prensky, 2012; Vaportzis et al., 2017). This literature review includes research
studies primarily published between 2016 and 2021. However, some earlier studies offer
insight into the older students’ population and their ability to be aware of, comprehend,
and learn with ever-changing digital technology. This section provides an overview of the
over-50 learners (also referred to as older learners), their learning needs, and technology
skills. In addition, there was a gap in the literature about the needs of these learners, how
they learn digital skills, and how best to support them acquiring these skills (Jacobson et
al., 2017; Schreurs et al., 2017).
Characteristics of the Over 50 Learner
Older adults are active learners (Uemura et al., 2018); most of their learning
occurs as self-planned learning projects (Henschke, 2016; Loeng, 2020). Moreover, many
individuals over 50 years of age use this period of their lives as a time of learning and
personal development (Uemura et al., 2018;).
Much discussion about digital literacy reflects assumptions about generational
differences (Prensky, 2001a, 2001b; Ross-Gordon, 2018). Costa et al. (2019) examined
34
social media use and beliefs among diverse undergraduate students in a quantitative study
using data to compare older learners with traditional college learners. The results showed
that older adult learners were more inclined to take all their classes online, start their
education at one institution, transfer later to another, and enroll part-time. Compared to
their younger college peers, older learners were more immersed academically, mingled
less with their peers and faculty, and found their college campus less supportive and
lacking a means of motivating students. On the other hand, these older adults were more
self-sufficient and wiser than their younger peers, albeit not as technologically competent
(Costa et al., 2019).
However, older adults can experience barriers to learning, including potential
physical limitations and cognitive and social deficits (Di Giacomo et al., 2018). Rangel et
al. (2015) explored the importance of instructor delivery style, more so than the actual
students’ dispositions, cognitive abilities, or even motivation in a study with 156
students. Rangel et al. surveyed students for a semester to capture the progressive
relationships between trainer and trainee, if any. Students’ assessments of the trainer’s
expressiveness, rather than their perceptions of the trainer’s competency, predicted
knowledge transfer intentions. Rangel et al.’s research looked at the experiential learner,
someone who has learnt a lot from real-life situations and is most likely a mature adult,
such as someone over 50. This learner is more likely to see learning as an internal,
experience-based, and personal process rather than a detached (external) process (Kolb,
1984). This research has implications for how to approach and teach older learners about
35
technology, such as including competent and active engagement by faculty, which allows
learners to reflect on themselves (Mezirow, 2009).
Although many older adults benefit from technology-supported learning, some
face challenges in adopting modern technologies (Pirhonen et al., 2020; Schreurs et al.,
2017). A study by Zheng et al. (2016) addressed diverse and varied findings related to the
factor of age (over 50) in technology-based learning to understand older students’
learning outcomes better. This study explored the concept of the “redundancy effect,”
which occurs when learners are exposed to various visual, auditory, and tactile content
such as text, imagery, and audio recordings. For example, the authors described a
learning module that included animation and narration versus animation, narration, and
on-screen text. Due to the redundancy effect, they discovered that movement with
voiceover and on-screen text induced irrelevant information processing. The addition of
on-screen text overwhelmed visual working memory, resulting in poor learning
performance. Furthermore, Zheng et al. discovered that movies with captions (on-screen
text) resulted in higher learning results for older learners than videos without captions. As
a result of a significant decline in working memory and processing speed, older learners
required additional sensory redundancy support in information processing.
Encouragement and reassurance by family and friends may be a factor for digital
literacy acquisition for older adults (Blieszner et al., 2019; Martínez-Alcalá et al., 2018;
Tsai et al., 2017). For example, Jin et al. (2019) found that such support was a strong
predictor for Internet use among older adults, including exploring an encouragement or
discouragement factor. It appears to be a function of expected benefits and risks.
36
Contrary to younger individuals, if the older adults perceived the benefits would
compensate for potential hazards, they were more likely to feel encouraged to learn
technology; private learning settings were the preferred method over professional
courses. Jin et al.’s (2019) quantitative study further examined the nature of the skewed
digital divide for adult learners over 65. Jin et al. (2019) concluded that age is the most
influential factor for an individual’s Internet usage, and with each added year of age, the
probability of internet usage decreases.
The internet can reflect an individual’s economic, social, and cultural activities
and parallels certain offline activities, including inequalities to access training within a
digital knowledge-based society (Kromydas, 2017). Tirado-Morueta et al. (2018) aimed
to confirm if a relationship existed between levels of internet use for adults over the age
of 55 and formal digital literacy support (DLS) programs available to those individuals.
Furthermore, they wanted to know if DLS would mediate or reduce the effects of a
perceived sociodemographic factor on internet use. Participants designated their level of
internet use in such activities as online shopping and banking, online forums and social
networks, and instructive exercises, including accessing news and searching for helpful
information. The findings revealed that an older adult’s sociodemographic characteristics
influenced their internet access and use. Age was not correlated with how much they
accessed the internet or the mistreatment they received, except in relation to the effect of
social position, which is connected to wealth, education, and social resources. Thus, the
digital divide between younger and older generations may not be as it seems, as it holds
much more complex factors, such as income and education levels.
37
Learning Expectations and Needs for Older Learners
Learning needs and expectations are vital in sustaining older adults’ motivation to
learn (Grunschel et al., 2016). Older learners’ behaviors focus on meeting specific needs
if they believe they can satisfy them; on the other hand, learning motivation decreases if
students do not clarify learning needs (Lai & Bower, 2019). Thus, considerations such as
supplying an active learning environment and encouraging self-directed learning may aid
in fulfilling the needs and expectations of the over-50 learners (Martínez-Alcalá et al.,
2018; Morrison & McCutheon, 2019). This section examines how older adults’ learning
expectations and needs are met within HEIs.
Older adult learners generally cannot easily adjust their busy lives to fit colleges’
fixed schedules. They prefer institutions where flexible programs and services
(MacDonald, 2018). Betts et al. (2019). Older adults’ descriptions of digital technology,
and experiences of digital inclusion sessions, were explored using qualitative methods.
Seventeen older adults (aged 54 and 85 years) participated in two focus groups that each
lasted approximately 90 min to examine how older adults experienced and comprehended
technology daily. The conclusions support the findings that this group of older adults is
aware of digital technology, is interested in developing more skills, and gaining a better
understanding with one-on-one training.
Instructional methods can support the unique needs of older students (Sharp,
2018). The addition of technology into everyday life has become the foundation of the
learning facilitation of the adult learner. However, how technology is facilitated in senior
learning populations is unknown. Chiu et al. (2019) illustrate the importance of
38
implementing an active learning environment for these older adults. This study explored
several case research methods focused on understanding employment, adjustment, and
revision of educational methods experienced instructors at senior learning centers. The
results show that the instructors used different teaching resources when teaching older
adult learners. The acquisition of technology skills is vital for the older adult learner
because of its prevalence across learning experiences (Chopik, 2016).
Martínez-Alcalá et al. (2018) found that for older adult learners to achieve
maximum benefits of technology, they must overcome challenges learning how to
navigate physical hardware, such as smartphones or an iPad. Martínez-Alcalá et al.
(2018) investigated non-traditional adult learners returning to school either after being in
the workforce for many years or attempting to complete a college degree after much time
away from an academic environment. Participants enrolled in an online developmental
writing course and completed a computer literacy assessment before taking the course.
Martínez-Alcalá et al. (2018) compared the older learners to traditional college students.
They concluded that even though their computer literacy scores were lower, the older
non-traditional students outperformed traditional students in course performance. Thus,
the technological deficiencies of the older adult learners did not affect their learning
outcomes negatively related to their ability to navigate the course successfully. The older
adults’ self-direction and motivation were critical factors in their accomplishments.
Aging adults often experience a decline in cognitive abilities, such as information
processing, learning speed, memory, language, and overall executive functioning (Kazazi
et al., 2018; Staff et al., 2018). Pappas et al. (2019) conducted a study of 103 older adult
39
learners aged over 55 to explore this population’s learning needs and investigate their
cognitive functioning and its relationship to adopting new digital technologies.
Information and communication technologies may have an important influence on the
daily lives of older adults. However, Pappas et al. determined that compared to younger
students, older adults lagged in their overall digital skills, ability to access the internet,
and understanding of and engagement with online learning activities (such as eLearning).
Pappas et al. determined that their study participants responded positively to course
content that applied to their lives as they preferred to practice what they were learning.
The eLearning aspect of this study revealed that learning modules needed to have clearly
defined learning outcomes for older adult learners, be concise, and use multimedia rather
than text-based designs.
Older students arrive at college with specific life experiences and expectations,
which instructors and trainers need to understand to improve students’ learning outcomes.
For example, it may be that HEI administrators assume students can use technology
(Martins et al., 2019). However, HEI administrators may be unaware of students’
expectations about technology use in their courses and their self-confidence with
technology (Ross-Gordon, 2018). For example, Cirule et al. (2019) used a mixed methods
approach to determine undergraduate students’ beliefs of their aptitudes using computer
technology. Results showed that students over the age of 25 perceived themselves to have
a lower aptitude for digital skills than those under 25. Still, they were interested in
increasing their knowledge and computer skills. Thus, besides traditional characteristics
40
of motivation and commitment, ambition, and goals, students entering college classrooms
must have a vast array of digital and technological skills.
Technology Skills of the Older Learner
There are unique challenges that impede some students over 50 from using recent
technology or accessing the internet. Nonetheless, despite challenges, many older adults
retain positive attitudes about technology and understand the benefits of being technically
proficient. This section reviews research about older adults and technology who use their
perseverance and wisdom to overcome technological challenges rather than allow
technology to impede their successes.
Older adults typically have fewer digital skills than traditionally aged students and
maybe less persistent in their technology use (Di Giacomo et al., 2018). Beringer (2017)
conducted a quantitative study examining the computer literacy skills of seniors and
explored the myth that this population does not use the internet. Beringer found that
while it’s assumed that this age group lacks digital skills, they were more likely to engage
if provided with foundational technological knowledge.
The digital divide concept is multifaceted due to various determining factors that
promote or discourage Internet and digital access. Studies have cited socioeconomic,
institutional, and even physiological factors as potential determinants. However,
technological determinism has not entirely explained the emergence and continuance of
the digital divide (Friemel, 2016). Lee and Kim (2019) used a mixed-methods study to
explore the model of intergenerational tutoring on digital technologies. A group of 55
older adults (average age approximately 70 years) underwent a series of six mentoring
41
sessions by younger undergraduate students. Using data from pre-and post-test surveys,
the researchers documented improvement in older adult learning, including digital
literacy, self-efficacy, and a desire to learn more about technology. Additionally, the
researchers found that older adults were less anxious and more confident when learning
experiences were tailored to their needs and preferences.
Researchers of the digital divide have focused on the have and have-nots in terms
of technology access (Hargittai et al., 2019). However, demographic factors such as age,
gender, and socioeconomic status may create the digital divide. Yoo (2021) investigated
how older adults are expected to use the Internet and mobile devices with Internet-based
services like education, health, and communication. Although this research illustrated an
increasing number of older adults embracing digital lives, they face unique challenges
due to age-related changes. Data showed that unique barriers make it challenging to keep
up with advances in technology. This study investigated an introductory course designed
for adult learners developing their digital literacy skills. This study highlighted the
importance of creating a quality learning environment for adult learners to lead a more
productive and enjoyable life with mobile devices.
Generationally, older learners may be less at ease with technology or learning
about technology outside traditional classroom training. This hesitancy to learn and adopt
modern technologies can impact the ability of the older adult learner to apply and transfer
knowledge and skills (Wang et al., 2019). Using a sample of 811 adults over the age of
65, Mostaghel and Oghazi (2017) explored these barriers to learning technology for this
older cohort surveying the participants regarding several impeding factors: self-efficacy,
42
anxiety, self-reported health conditions, cognitive ability, and physical functioning. The
results showed that apprehension and anxiety levels when facing new technology tools
were significant barriers to learning for most of these seniors. This group of educated
older adults who used a variety of tools that could accelerate their acceptance of
innovative technology. The results indicated that the more an older adult was aware of
their existing knowledge about technology skills the bigger the challenge in acquiring
new skills. This may increase anxiety about improving new technology capabilities. This
study is one of the few to recommend professional psychologists as potential resources to
supply guidelines for the elderly to cope with their anxiety and apprehension toward
learning modern technology.
Some researchers have focused on the digital divide related to older adults’
technology access disparities versus their younger counterparts (Mitchell et al., 2019).
However, merely having access to technologies does not always lead to actual use and
adoption (Blackstaffe, 2017; Godoe & Johansen, 2012). For example, Gatti et al. (2017)
examined the generational digital divide that might be seen as a reaction to older people’s
physical and mental decline. Due to societal costs, however, there has recently been focus
in connecting the generational digital divide, and many studies have indicated that tablets
seem to support the older adults due to functionality and features that accommodate their
capability to connect, to be self-sufficient, and to be fully independent; it may enhance
their well-being. Findings suggested how to boost self-efficacy while improving learning
perspective and tablet usage. The participants’ perceptions of their learning process and
success in the course, as well as their digital self-efficacy, were investigated using a
43
qualitative-dominant co-occurrent mixed-methods approach (50 individuals over 65 years
old). Even though learning modern technology may be demanding or inaccessible for
older adults, such learning can aid older adults’ inclusion into the digital society
(Vaportzis et al., 2017). A study by Tsai et al. (2017) explored how older adults learned
to use a certain technology and the importance of social supports in this process using a
qualitative method. The authors discovered that using technology requires a variety of
cognitive, physical, sociological, and emotional abilities needed by adult learners to
succeed in digital environments. Older adults needed support for the initial set up of their
devices and support for learning to use them. Social support in the environment played a
crucial role in supplying this help to the older adults through family, friends, and other
acquaintances. Support for older adults to sustain socially engaged lives and access the
Internet may encourage practical and efficient digital literacy skills as they continue to
age. Tsai et al. (2017) perceived ease of use when utilizing technology acceptance and
adoption model (STAM) as the learning model for older learners. These two learning
models, STAM and the social cognitive theory (SCT) (Bandura, 1977, 1994), was such
that, within their study, Tsai et al. (2017) found both models helped to understand older
adults’ learning processes. Support during the learning process from family or
professionals was crucial to provide older adults the confidence to experiment and learn
new functions. This finding confirms the need for social support for older students to gain
motivation and self-confidence in their technological learning abilities.
Many older adults are deficient in the required digital literacy skills needed to
obtain the benefits of digital tools, such as health tracking (Gordon & Hornbrook, 2018).
44
Gualtieri et al. (2018) examined 100 participants aged 50 to 75 about their abilities to use
a specific digital application that recorded physical activity as a potential health benefit.
There were several barriers to the successful use of the smartphone devices used in this
study. Primarily, individuals with older model phones could not download the tested
application. In addition, many participants required much assistance to download and set
up the application; vocabulary such as “sync with Bluetooth” confused these older adults
needing clarification and assistance. However, Gualtieri et al. discovered that once these
older learners overcame barriers to set-up through hands-on training and support, their
motivation and enthusiasm in using the specific application increased. In healthcare,
obstacles to digital technology can hinder the adoption and use of digital health
technologies that rely on smartphone applications.
Some research indicates social implications for older adults’ technological
abilities and access as younger adults engage in more technology-based communication
(Tyler et al., 2020). Using a self-report questionnaire provided to participants, Hunsaker
et al. (2019) explored if the oldest adults would be less likely to engage in a formal type
of digital exchange, such as a church group’s website, a health club, or another
organization to which they belonged. Mannheim et al. (2019), through examining
demographic differences related to technology use, found variances in technology use,
favorable views of technology, and comfort using technology based on age. Younger
users were between 18-28, while older adults were over 60. Older adults were more likely
to experience growth only in those instances when they had to use technology for a given
task. Individual skill levels influenced whether they used technology; younger users were
45
more likely to have technology-related skills. Older adults, aged 60 to 91, scored the
lowest in technology skills, followed by middle-aged adults aged 40 to 59. Older adults,
especially those over the age of 60, unlike their younger counterparts, used technology on
an as-needed basis, while the younger participants integrated technology into their daily
activities. Thus, college students over 50 may require unique support from their higher
education institutions, such as offering introductory computer courses, mentoring on
navigating the institutions’ websites, and possibly creating peer support groups (Tyler et
al., 2020).
Technology, Learning, and the Over 50 Learner
Informal Technology Learning for College Students Over 50
Older adults have managed the benefits of technology use versus the potential
risks (e.g., risk of failure) (Andrews et al., 2019). This section reviews how older adult
learners have successfully learned computer and Internet skills outside formal academic
settings. Morrison and McCutheon (2019) conducted a focus group study of 17 older
adults, with a mean age of 72 years, to determine whether digital technology functioned
to disempower or empower older adults. Findings indicated that older adults described
the value of technology as empowering because it eased their daily activities and aided
them in keeping certain social relationships.
Nonetheless, these older adults also recognized that technology might also
disempower them. They noted that without the proper skill sets or an ability to overcome
their fears and anxiety associated with technology use, the digital divide between
generations was likely to widen, potentially increasing their social isolation and
46
diminished access to vital online services (Oliver et al., 2017; Uemura et al., 2018).
However, this group of participants was optimistic and held that learning via peer groups
was their most comfortable method of learning modern technology.
An individual’s healthcare information is increasingly being provided through
web-based mediums, creating access to information about maintaining or improving
health and managing diseases. This new approach is readily available to all age groups
and particularly critical for older adults (Gordon & Hornbrook, 2018). A study of older
adults aimed to improve eHealth literacy proposed that collaborative learning, or peer-to-
peer learning, developed and tested in public libraries, was the most favored method for
the older adult student of technology. Kara et al. (2019) confirmed that collaborative
learning was a beneficial method for older adults’ learning new technological skills,
wherein both the social environment and the personal competencies of the learners
situated the environment for valuable learning to happen. As noted by the researchers,
participant ages ranged from 61 to 84 years, bringing to the forefront whether influences
proven to be effective for the “younger” segment of this older population could be
generalized to the “oldest” old.
With the pervasiveness of digital technology in many aspects of society, specific
learning activities involved with technology have become essential to the learning
processes of older adults (Reneland-Forsman, 2018). Chiu et al. (2019) conducted a
multi-case study within adult community learning centers to identify the teaching
strategies for older adults and substantiate learning transference. They found that
unintended incidents occurred in the teaching of older learners. When faced with such
47
incidents, the age difference between teacher and students may create a barrier to
learning. On the other hand, this shortening gap between the instructor and older students
enabled the senior adults more learning opportunities. In addition, when teaching Internet
technology, instructors may increase learning if they convert professional terminology to
the everyday vocabulary of the elderly participants in the classroom.
College students over 50, as do all generations, use technology daily for multiple
reasons thus need knowledge of relevant technology tools and skills (Blieszner et al.,
2019). In a study by Seo et al. (2019), the researchers used a mixed methods approach to
conduct seven focus groups with older African Americans who were low-income adults
aged 55 and over to understand older adults’ experiences, knowledge, awareness, and
needs in terms of digital skills and literacy. Seo et al. determined that participants showed
strong motivation and commitment to learning new technological knowledge and skills.
These researchers used the term “technological capital,” a concept based on an
individual’s awareness, understanding, access, and technical capacity (Carlson & Isaacs,
2018). The older adults in this study were inspired, motivated, and enthusiastic as they
increased their technological capital as a result.
Some researchers have explored the role of technology and its impact on the
potential social isolation of the elderly (Administration on Aging, 2018). For example,
Delello and McWhorter (2017) conducted a mixed-methods study to explore whether the
information or communication technologies (specifically iPads) might enrich the lives of
135 adults ages 61 to 99 adults. Throughout the study, the researchers performed training
on iPads for these older adults. Before the exercise, less than 20% of the participants had
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used an iPad. However, by the conclusion of 6 weeks of iPad training, 90% of the
participants felt proficient using an iPad, not only a significant increase in iPad
competency but allowing these older adults an added venue and tool for staying
connected with friends and family.
Some factors encourage or discourage the reception of technology by older adults
(Vaportzis et al., 2017). Ma et al. (2016) explored smartphone usage through a mixed-
methods study, including pre-questionnaires and personal interviews with 120 older
Chinese adults. Age, education, marital status, employment position, source of income,
and economic standing each had substantial effects on perceived ease of use and attitude
toward smartphone technology. Personality factors were significantly linked with age,
demonstrating that ‘younger’ older individuals were more willing to accept cellphones.
These behavioral constructs were favorably impacted by education, indicating that a
higher level of education may lead to a more positive attitude toward smartphone
technology. All behavioral variables were adversely connected to marital status, implying
that non-widowed older persons were more positive about using smartphones. The
researchers also discovered that financial circumstances had a positive impact on
adoption, with older adults who were financially comfortable being more willing to
accept cellphones. Self-satisfaction and relaxing conditions were the key factors
impacting user satisfaction and convenience of use with smartphones for this group of
older adults, out of all the findings of this study.
Smartphones utilize similar touchscreen technology like iPads, personal
computers (P.C.s), point-of-sale systems (when paying at a store), or teleconferencing.
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Chiu et al. (2016) explored older adults’ learning needs, attitudes, and the impact of using
‘entertainment’ apps on older adults’ physical and psychological well-being when using
touchscreen apps on smartphones. Their mixed-methods study presented an 8-week
training course designed for a sample of 39 older adults who had or did not have Internet
experience. A focus group discussion followed this. After the class, results indicated a
substantial decrease in the participants’ anxiety levels about Internet use. The study also
demonstrated a decreasing trend in the participants’ concerns about certain negative
perceptions of Internet usage, such as failing eyesight, feelings of fatigue, or lessening
their ability to communicate in person Chiu et al. (2016). Thus, suitable training aided
older learners in easing learning anxiety regarding technology as it lowered their
depressive symptom scores compared to baseline scores. It was also noted these older
adults learned new practical skills but at a somewhat slower pace than their younger
counterparts similar to the findings of Chiu et al. (2016).
Cognitive decline is a normal aging process; faculties such as reasoning, memory,
and information processing generally decline gradually as one age (Willinger et al.,
2019). Researchers have explored such abilities, or lack thereof, in older adults and
technology-supported learning research. For example, Ware et al. (2017) explored digital
learning of a second language as a cognitive stimulant for a group of 14 senior citizens
(exact ages were not disclosed) to discover if blended learning incorporating technology
effectively mediates some expected cognitive decline. This type of learning was a
challenge for some participants, while it eased the learning process for others. Overall,
the blended and multimedia methods were beneficial for teaching and motivating these
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older individuals to learn a second language. The participants were encouraged to use
tablet devices (such as iPads) to look up words reassuring ongoing involvement by
supplying the older adults with a tool for self-directed learning. The researchers
concluded with a training program and adequate participant motivation. This second
language learning method could be an intervention for those with cognitive impairment
and even perhaps mediate some of the preliminary stages of dementia.
Due to a certain amount of cognitive decline in older adults, their needs and
concerns in learning computer usage and technology may differ from those of younger
individuals. Hulur & Macdonald (2020) case study explored how older adults responded
to technology training and motivated them to learn. Participants were two adult educators
and four older learners. Hulur & Macdonald (2020)
concluded that older adults learned
modern technology best when the instructor talked them through each step while
completing each specific task.
HEI Technology Use by Learners
Colleges and universities try to retain students as supporting high graduation rates
is essential to an institution’s reputation (Banks & Dohy, 2019; Barr, 2016; Lee & Kim,
2019). However, researchers have found that undergraduate students experience
difficulties understanding and using technology, occurring most often within the
demographic of students over 50. As a result, they are likely to experience lower grades
and higher attrition rates (Banks & Dohy, 2019). This section reviews current research
focusing on recent innovations in university use of technology for all students and older
learners and how they learn technology in higher education.
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Technology Innovations and Student Learning. Because Internet information
and communication technologies are transforming much of society, there is little reason
to believe they will not be defining factors for innovation within higher education in the
coming decades (Department of Education, 2017). Three recent innovations illustrate the
use of technology in MOOCs (Andone & Mihaescu, 2018). In addition, HEIs have used
learning technology in multiple ways. This section reviews research on MOOCs and
several widely adopted HEI tools used to support learning.
HEIs began to use massive open online courses (MOOCs) in the late 2000s as a
strategy to provide educational resources to a broader and more global population
(Lambert & Hassan, 2018). Since then, MOOCs have provided researchers with many
avenues for research. For example, Joo et al. (2018) examined 222 university students’
motivation and enthusiasm to use MOOCs, specifically K-MOOCs (Korean MOOCs).
The researchers investigated the use of MOOCs from several approaches, including
learners’ motivation to persist, self-determination, perceived ease of use, and overall
satisfaction. For first-time MOOC users, student satisfaction had a significantly positive
influence on their intention to continue using MOOCs. This indicated learners needed to
feel fulfilled and rewarded with their first MOOC experience if they planned to use
MOOCs in the future. Thus, older students’ perceived usefulness and ease of use can
contribute to these older adults using new learning technologies such as MOOCs.
De Hart and Wentzel’s (2020) research illustrates the efficacy of podcasting as a
valued and worthy educational resource (Goldman, 2018). Rosell-Aguilar (2013)
surveyed almost 2000 users of iTunes U, emphasizing participants who studied new
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languages versus those who learned something other than a new language. Most prior
studies regarding podcasting as a teaching and learning tool were undertaken with digital
natives (teens to early 20s). However, this study surveyed adult users of all ages. Student
ages ranged between 25 and 54 for non-language learners (learning something other than
a new language) and between 55-64 for those learning a new language. Since that time,
there has been no further wide-ranging study on users of iTunes U. Findings indicated
users placed value on the quality of the materials available via iTunes U, and they
believed the materials helped them learn. In addition, the students over 50 listened to
their coursework podcasts on mobile devices, in contrast with findings from prior
research. Thus, older students in this study sought alternative technological learning
methods as they kept pace with technology.
Alternative learning methods include video and audio podcasts (digital files
distributed through the Internet using personal computers or other mobile digital devices),
which have evolved rapidly in higher education due to pedagogical possibilities (Al-
Ismail et al., 2019). Jiménez-Castillo et al. (2017) examined factors influencing the
integration and transfer of knowledge when video podcasts were used as complementary
tools to earlier and equivalent conventional lectures on the topic. One of Jiménez-Castillo
et al.’s hypotheses stated that “Students’ perceived prior knowledge gained from classes
has a positive influence on their perceived assimilation” (p. 450) of related content
presented in video podcasts. In addition, these researchers found that the ease of use of
video podcasts positively affected the perceived usefulness of the podcasts and positively
influenced learners’ behavioral intention, acceptance, and use of video podcasts.
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Mobile learning (mLearning) is a method that not only incorporates podcasts but
includes the use of such devices as laptops, digital tablets, personal digital assistants, and
mobile phones (Sutton & Desantis, 2017). mLearning has become a tool with vast
potential in both classrooms and informal learning outside the classroom. For example,
Gezgin (2019) investigated the effect of mLearning support on students’ academic
success using a database management system course. According to the findings,
mLearning positively affected students’ academic achievement for the course; the cohort
of students supported by mLearning was more successful than those supported only by
face-to-face training. Further, mLearning students emphasized their motivation, overall
interest, and curiosity in the effects of the mLearning approach on their academic
success.
In 2010, the iPad impacted learning approaches and instructor practices (Stec et
al., 2020). Using a qualitative case study design, Islim and Sevim-Cirak (2017) explored
faculty members’ educational use of technology, particularly iPads, and sought their
opinions of the educational benefits of technology and their students’ technological
competencies. Results showed that faculty members used various devices such as iPads,
and multiple applications within their classrooms based on their class needs. Despite
most faculty participants being digital immigrants, they saw themselves and their current
students as technologically capable. These participants reported experience,
socioeconomic status, and enthusiasm to use technology affected technological
competence for themselves and their students, particularly given the affordability and
mobility of iPads.
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Formal Technology Learning for Students Over 50. HEIs consider digital
literacy skills necessary for successful learning (Kvavik, 2005; Newman et al., 2018).
Vaportzis et al. (2017) found that technology was an ever-changing and anxiety-ridden
challenge for many adults over 50. This section reviews how seniors have successfully
learned computer and Internet skills within academic settings.
Research in this section suggests that the definition of the learner over 50 years of
age in higher education has changed over as it relates to their technology capabilities
(Dauenhauer et al., 2016; Guest, 2017; Martínez-Alcalá et al., 2018). It may be that as
baby boomers returned to school, they brought greater digital literacy as they
demonstrated the capacity to learn recent technologies (Torun, 2020). However, HEIs
cannot assume older students are digitally literate.
As technology becomes ubiquitous, it supplies opportunities to promote
intergenerational connections across demographically different populations and diverse
contexts (Boger & Mercer, 2017). Like the study by Lee and Kim (2019), Dauenhauer et
al. (2016) administered surveys. They held focus groups with a sample of 132 older
adults and the second sample of 20 graduate students who worked alongside the older
adults to explore what is known as intergenerational service eLearning. The elders in
Dauenhauer et al.’s study wanted to learn computer systems and technology
advancements from younger generations. Such knowledge and skills were much more the
expertise of those more youthful individuals. Dauenhauer et al. found that older adults,
most of whom were well-educated, preferred one-time lectures on learning technology
versus committing to an entire course. Still, all were fascinated by the ability to interact
55
with younger students who were learning the same content. Those graduate students also
received help from this type of intergenerational higher learning, cultivating their interest
in working and learning alongside elders within their communities. In this
multigenerational setting, the older adults stated they learned more effectively. In the
same way individuals from diverse racial, ethnic, and gender backgrounds learn to
respect and value differing perspectives and contributions, so do people from various
generations in learning situations discover and appreciate the vantage point of those older
or younger than themselves, a concept known as “reverse mentoring” (Zauschner-
Studnicka, 2017).
Many older adults believe they are incapable of learning to use technology
(Kuerbis et al., 2017). To explore a means of overcoming this barrier, Martínez-Alcalá et
al. (2018) utilized a blended teaching approach so that 98 older adults might develop and
improve their digital skills more quickly. One-half of the participants took part in face-to-
face workshops, while the other half partook of a blended learning model. As a result,
digital literacy increased post-evaluation after the face-to-face workshops, but
significantly more so with the combined method. Furthermore, the results regarding the
efficacy of the blended workshop confirmed older adults confirmed ease of use,
perceived usefulness, attitude toward using, and intention to use technology. The
combination of classroom instruction and independent study was beneficial. Moreover, It
may be that motivation to learn new technology skills and understanding the usefulness
of the skill can enhance digital literacy.
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Older adults tend to show less expertise after technology training and take longer
to learn than their younger peers; thus, extending the length of activities and examining
specific training techniques may better support older adults (Lee & Kim, 2019). Miwa et
al. (2017) pursued the effects and retention of knowledge for older adults taking digital
learning courses over three years. Participants were divided into two groups: up to 60
years of age and more aged than 61. The researchers found that learners significantly
improved their computer skills after taking a digital learning course based on self-report
surveys. In addition, those who used computers and accessed the Internet frequently were
more likely to retain the skills learned in the digital learning course over three years than
those who did not use a computer often. This study indicated that continuing education or
ongoing learning courses to be cornerstones in retaining digital literacy skills for older
adults.
In the past, some university administrators viewed older adults as incapable of
learning due to declining intelligence, memory, and sensory abilities (Lee & Kim, 2019).
Zhang et al. (2017) questioned this generalization about older learners within higher
education. Using the sociocultural theory and situated learning models, these researchers
came to similar conclusions as Dauenhauer et al. (2016). Zhang et al. matched older
adults with younger cohorts within specific study areas. Findings indicated that sustained
conversations with younger peers helped older adults understand unfamiliar concepts and
phenomena. Although the results of this study cannot be generalized to other settings,
they reinforce the idea that social relationships among a variety of generations of learners
57
allow older adults to participate in collaborative learning supported by situated
instruction from younger learners.
Many older adult learners can educate themselves in digital technologies but may
be susceptible to anxieties that arrive with the new technology use. These anxieties may
include psychological issues stemming from learning something unique and foreign to
their prior experiences (Vacek & Rybenská, 2017).
Ball et al. (2019) investigated the demographic group with the most significant
digital divide being the elderly. The researchers examined how older persons perceive the
physical use of information and communication technology (ICTs), focusing on how
perceptions differ across generations and circumstances. Nine focus groups provided data
for this study. Seniors admitted that ICTs helped them connect with social relationships
that were geographically distant, but that they also caused them to feel disconnected from
social ties that were geographically close. This phenomenon is known as the “physical-
digital gap,” It occurs when a group feels excluded or offended when individuals around
them use ICTs while they do not or cannot use ICTs. Older generations are typically
referred to as digital immigrants (Prensky, 2001a) because their preferred mode of
communication is physical face-to-face encounters and conventional manners. However,
there are suggestions for bridging the physical-digital divide. However, they were not as
technologically savvy as their younger counterparts. Thus, older adults bring many
practical skills to college learning, compensating for their lack of direct technological
knowledge. The older adults in this study appeared resilient, yet other facets of college
life, such as academic performance and perseverance, are significantly impacted by social
58
anxiety (Boukhechba et al., 2018). Thus, there may be unknown factors impacting digital
literacy.
Online education is attractive to older adult learners looking for opportunities to
obtain degrees while working and tending to other life commitments (Simmers &
Anandarajan, 2018). Alqurashi (2019) examined the relationship between older adult
students’ satisfaction with the technology used at their online university or college and
their intent to continue online education. Using an online survey adapted from the
technology acceptance model, 300 older higher education students contributed to the
study. It was determined that learner satisfaction was a significant predictor of older
learners’ intent to continue learning, second only to learner motivation to pursue their
education. Thus, in this case, learning technology can occur when the learner is motivated
to learn the subject matter.
There are many advantages to online learning, including providing equal
opportunities for learners to learn at their chosen pace (Liu et al., 2020). For example,
Farhan et al. (2019) explored perceptions of 36 adult learners toward e-learning in higher
education using focus group interviews and semi-structured surveys. The results
indicated the qualities of self-discipline and effective time management were vital for
older students having a multitude of outside school responsibilities (family, jobs, etc.) as
they took part in an e-learning course. Furthermore, the results noted older adults
required technical training or other preparation before learning online or in a blended
setting.
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Online and blended learning has become a preferred mode of learning,
particularly for some older adults, since studying can be carried out at any place and at
any time (Blieck et al., 2019; Galanek et al., 2018). Olivares-Cuhat (2018) explored the
suitability of blended learning for older adults learning a second language using
technology-enhanced language learning tools. Olivares-Cuhat’s study discovered that
rather than impeding the learning of a second language for older individuals, proper
implementation of the tools in a blended training environment facilitated the academic
success of older students.
Older adults may not be as quick to learn technology as their younger
counterparts. However, these older individuals may compensate for their lack of learning
speed through abundant life experiences. Boakye (2019) examined 20 more aged
architecture students accustomed to traditional hand-drafting methods and techniques and
challenged by fast-paced technological innovations. Using a basic qualitative design,
Boakye identified a necessity for detailed coordination among factors outside the learning
environment itself, including interactions with school administrators, teachers, and even
the technology software companies who had designed the software used. In addition,
these technology companies could contribute to the older learners’ professional
development by providing internships, reinforcing and encouraging positive experiences,
and supporting intellectual growth.
Positive experiences and feeling comfortable with technology are a part of the
older adults’ needs when learning, and online learning can also facilitate the development
of social capital and inclusion (Diep et al., 2017). Rabourn et al. (2018) sought to
60
understand the college experiences of adult learners over the age of 50. The research
sample included 20 adult community college students, and data collection included semi-
structured interviews. Findings indicated older adult students to feel they fit in, they
needed to feel comfortable and welcomed within the learning group and environment.
Diep et al. (2017) confirmed these findings in online and blended environments that
allowed older adults to establish social capital, increasing inclusion, which was less likely
to occur in traditional learning environments with mixed generations.
For older adults, fear of making errors, or security concerns, is a barrier to
learning digital technologies (Knowles & Hanson, 2018). Huyler and Ciocca (2016)
explored these issues to understand how technology can support learning for older adults.
They determined a primary contributing factor for older adults’ adoption of learning
strategies using mobile devices, such as text messaging, required a rationale for applying
the tool. Thus, older adults may be more compelled to adopt learning technologies when
they have a cause for how technology learning can support academic learning.
Mobile technology, platforms, and the ease with which people can access the
internet have made it easier to get information and span the technology gap. Ugur et al.
(2016) examined the factors that influenced Turkish college students’ acceptance of
mobile learning, presented an extended model, and analyzed the factors that influenced
their acceptance. Students’ intrinsic motivation to use mobile learning was a key success
factor in the mobile learning adoption process.
This trust and protection of personal information factor may be essential to how
older students approach digital technology (Elueze & Quan-Haase, 2018). Hamidi and
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Chavoshi (2018) investigated technology usefulness, ease of use, and behavioral
intention. In their analysis of the essential factors for adopting mLearning in higher
education, Hamidi and Chavoshi used a case study of 300 older college students. In
addition to traditional structures of technology adoption such as ease of use and
usefulness, the aspect of trust was a significant determining factor for the adult students’
acceptance of a given technology.
One of the appeals of mLearning for older adults may be multimedia options that
enhance learning. Hao et al. (2017) surveyed 292 adult college students to explore the
effectiveness of video lessons delivered for use on a mobile device. Video learning is a
sub-group of the broader category of online learning. Within Hao et al.’s study, the
discovery was that several factors significantly affect students’ behavioral intention to
adopt technological learning. Those factors included that mobile apps should be easy to
navigate when working on learning tasks. In addition, it should be easy to learn how to
use a new mLearning application. Finally, it should be easy to become skillful at using a
mLearning application. Thus, for video lessons to be practical via mLearning, a safe and
supportive learning environment may improve outcomes.
Technology Support Strategies for Older Learners. In HEIs, instructors have
used digital technology using a variety of innovative learning approaches to engage their
students (Sutton & Desantis, 2017). However, as higher education shifted from the use of
standalone desktop computers to the utilization of mobile devices - such as laptops,
tablets, and smartphones - students’ usage of these types of devices (Sutton & Desantis,
2017) suggests a need for support strategies targeting older students. Upon entering a
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mobile-intensive learning environment, older learners may be at a disadvantage
compared to younger learners who may have a higher level of digital literacy and hence
do not require as many supports.
Mobile computing, social technology support, and video lectures are just a few
ways college educators support students in meeting the challenges of new forms of
knowledge transfer and acquisition approaches (Zorn et al., 2018). Instructors have used
mobile devices as learning strategies and support tools to promote and reinforce learners’
needs and encourage participation in novel ways. Yet, older students may not engage in
these activities equitably. This section focuses on an area that may influence digital
literacy acquisition for older learners: effective technology support strategies.
Older college students lacking technological awareness and engagement may
require support or strategies that their younger counterparts do not (Vaportzis et al.,
2017). To counteract deficiencies for older students, some HEIs have designed unique
programs to aid older adults in accomplishing their educational goals. In a quantitative
study, Bahr et al. (2021) examined in this study, adult students were identified in a
community college’s student body. The gradations of experience, responsibility, and
subject feeling of adulthood that characterize this population of adult learners were
illustrated to show the differences. Bahr et al. (2021) found evidence on adult students’
participation in higher education and how their approaches to college differ from younger
students. Data showed that community college programs and efforts to improve adult
students’ achievements are examined.
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Behaviors such as enthusiasm and positive feelings toward technology usage,
along with the absence of fears and doubts, have allowed older people the ability to
manage using innovative technology (Anderberg et al., 2019). González and Morales
(2019) explored how behaviors and attitudes toward learning digital technology
supported older people’s successes in a course for learning basic computer skills. The
results showed that older individuals remained eager to learn about computers if they
remained physically and mentally healthy. Essential supports for learning to use
computers for various activities promoted older adults’ self-confidence and self-
assurance. Understanding and improving behaviors and attitudes may be a strategy to
encourage continuing education for students over the age of 50. As with other studies
relating to older adults and effective technology learning (Chiu et al., 2019; Gezgin,
2019), these participants had open minds and felt at ease once introduced to the computer
software and hardware used in the course.
Although some older learners displayed a reduced interest in learning, their
outcomes were better when content was adapted to specific cognitive styles provided
within the eLearning environment. Cognitive styles are a person’s typical problem-
solving method, thinking, perceiving, and processing of information (Sinnott, 2018).
Hence, when approaching and supporting older adults’ learning experiences via
eLearning technology, instructional designers and faculty might consider the cognitive
style of each learner (Gezgin, 2019).
With the expanded use of online pre-recorded lectures, Stull et al. (2018)
examined instructor recordings for video lessons using transparent whiteboards, an
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innovative tool for eLearning. Stull et al. explored learning experiences with traditional
whiteboards where the instructor faced away from the camera and the audience. They
also used a transparent whiteboard in which the instructor stood behind a clear
windowpane facing the video camera. At the same time, the instructor wrote or drew on
the glass, and the camera reversed the instructor’s writing and drawing so that it was
readable for the audience. In this way, learners viewed non-verbal cues such as facial
expressions and hand movements of the instructor. Findings indicated that students who
viewed online video lessons using transparent whiteboards performed better on learning
assessments and rated higher levels of social partnership with the instructor than those
who knew via traditional whiteboard methods.
Because of their potential deficits in digital literacy, older students need
institutional support (Martínez-Alcalá et al., 2018). Stone and O’Shea (2019) found that
supporting older adults and putting them at ease in a learning environment enhanced their
ability to learn digital literacy skills, particularly when learning to use mobile devices.
Additionally, programs and workshops about mobile devices provided by younger peers
(Dauenhauer et al., 2016; Kara et al., 2019) allowed for social support and an improved
and less-pressured learning setting. Bennett and Kapusniak also found that supplying
broad-minded support methods, such as one-on-one instruction, tutorials, or printed and
detailed instructions, aided older adults to be successful in college.
Specific challenges for mLearning include how and who provides instruction
about its use and maintenance (Lall et al., 2019). Asiimwe et al. (2017) called attention to
the reduction in face-to-face interaction among students, one reason for the high dropout
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rates in distance education. Research on digital divides indicates individuals who are
overlooked or discounted as learners (such as those who are disabled, or economically
disadvantaged) may be further prevented from full participation when ICT is used in
learning. They cannot afford the technology or access the personalized assistance they
might require (Friemel, 2016). Marginalized students are also often unable to use the ICT
due to higher education institutional failures to comply with legal and technical
requirements for impaired and disabled students (Brown et al., 2021).
Challenges of Technology-Based Learning for Older Students
This section discusses older students’ experiences and challenges when learning
new technology. Each type of delivery method of technology-based learning may not
work for all learners. For example, mobile technology has become crucial for HEIs due to
the wide variety of its benefits (Kvavik, 2005). When university systems integrate
mLearning into their educational programs and courses, they provide students access to
learning anytime and anywhere. In addition, research indicates students using mLearning
understood more effectively than students presented with just face-to-face teaching
methods (Gezgin, 2019). Nonetheless, for the older student, mLearning, and other
technology-based strategies, may prove problematic.
Often mobile technological features can be overwhelming and intimidating for
older users who cannot appreciate such features because they may not understand their
usefulness (Khawaji, 2017). In addition, older adults voiced concerns with mLearning as
having anxiety about a deficiency in direction, guidance, and support when utilizing
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mLearning (Vaportzis et al., 2017), even though mLearning has made learning simpler
for collaboration and sharing ideas using the Internet (Lall et al., 2019).
Pimmer et al. (2016) conducted a systematic evaluation of empirical studies on
mobile and ubiquitous learning, which indicated the benefits of student use of mobile
devices and technology-facilitated delivery of learning materials. Furthermore, as
learners attempted and practiced the technologies utilized, these individuals became more
engaged and active in and across college classrooms. Thus, the blending of situated and
collaborative learning methods using mobile technologies and devices may create new
educational opportunities for both young and old.
It may be that the portability of mobile devices enables the communication
between learners and learning material, their fellow learners, and educators (Lall et al.,
2019), thus overcoming challenges often experienced by older adults. Al-Emran et al.
(2016) examined the use of mobile devices within the HEI setting as they investigated
quantitatively various factors involved with usage among both students and faculty.
These authors considered gender, level of study, smartphone ownership, and age. They
found no significant difference between ages and attitudes using mLearning. However,
results demonstrated positive attitudes by students toward mLearning and revealed
college students are motivated and encouraged to use mobile technology within their
academic studies regardless of their ages.
Older students may overcome some challenges by using their skills and
capabilities acquired through their life experiences. For example, Babb (2021) examined
3,000 learners over 50 in an online science, technology, engineering, and mathematics
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(STEM) class, studying several specific traits, including ethnicity, gender, and non-
traditional student status. Findings indicated that older students performed significantly
better in the online learning environment. The researchers could not determine which
characteristics (motivation or self-directed learning skills) caused the older users to be
notably apt in the online environment. However, there was a relationship between age
and online content. Older students performed significantly better online than their
younger counterparts, with a higher attrition rate. It may be that college students over 50
preferred the autonomy of an online course, while traditional students were not prepared
to learn independently.
Summary and Conclusions
There are emerging accounts of the conditions that affect the older college
students’ engagement and success in learning digital technology and how best to support
these students in acquiring technological and digital skills (Jacobson et al., 2017;
Schreurs et al., 2017). In addition, research has shown that teaching older students has
expanded to include tools and strategies (McKenzie, 2019) and mLearning (Hofstede et
al., 2017; Information Resources Management Association, 2016). This literature review
has provided insight regarding older adults’ learning new technology, including their
motivation to learn and commitment to the topic at hand (Lambert & Hassan, 2018;
Martínez-Alcalá et al., 2018), the importance of the delivery style of the subject matter
(Rangel et al. (2015), peer mentoring by youngers students (Dauenhauer et al., 2016; Seo
et al., 2019), and the design of the technology being used (Tsai et al., 2017).
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A blend of teaching approaches, such as students collaborating with peers, both
young and old (Dauenhauer et al., 2016; Seo et al., 2019), along with more formal
methods, for example, assessment milestones and training students on basic technology
terminology (Chiu et al., 2019) have shown to be successful learning and support means
for older adults learning digital technology. Learning via mobile devices in an informal
collaborative setting using an inquiry-based method has also shown to be a successful
approach to learning for older adults (Khalaf et al., 2018). On the other hand, these
students are all unique individuals with a wide range of learning styles and cognitive
capabilities (Bendall et al., 2016). For an older adult to explore learning and using
modern technology, teaching methods should engage students in proven and effective
learning methods (González & Morales, 2019).
However, this literature review recognizes the limited scope of existing literature
regarding personal perceptions of college students over 50 and their experiences with
learning and using technology in higher education and informal settings (Jacobson et al.,
2017; Schreurs et al., 2017). While instructive and informative in its depth and breadth,
this literature review has illuminated limited evidence on the personal beliefs of those
over age 50 who have met and overcome challenges when entering new learning
environments, particularly those within higher education. This current research will add
to existing studies on college-level performance and persistence in adults over the age of
50 by exploring those individuals’ subjective experiences. There is a gap in the literature
about the needs of these learners, how they learn digital skills, and how best to support
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them in gaining these skills (Jacobson et al., 2017; Schreurs et al., 2017). This study’s
results will help close that gap through generic qualitative research.
This study used a basic qualitative method to explore the perceptions and beliefs
of the over 50 students whose digital literacy experience involved transformative
learning. Chapter 3 details the study method and design.
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Chapter 3: Research Method
The purpose of this study was to explore the insights of college students over the
age of 50 who attended traditional 4-year institutions to understand their support needs,
shared experiences, and types of learning used for digital literacy development. This
chapter includes a description of the study’s research design and rationale, the
researcher’s role, the study’s method, issues of trustworthiness, and ethical procedures.
Research Design and Rationale
The purpose of this basic qualitative study was to identify and better understand
the learning supports that are most helpful for college students over the age of 50 as they
acquire digital literacy. The shared experiences of this group would provide insight into
their unique needs to achieve digital literacy. The research questions were the following:
RQ1: How do college students over 50 years of age describe their learning as they
develop digital literacy skills?
RQ2: What types of support do college students over 50 years of age report most
helpful in developing digital literacy?
The core constructs of this study were digital literacy, transformative learning,
and supports for attaining digital literacy. Digital literacy is an individual’s ability to
comprehend and process information from a wide range of sources as presented via the
internet using various devices such as tablets, phones, or computers (Ray, 2018;
Techataweewan & Prasertsin, 2017). To attain digital literacy, learners need supports for
acquiring the appropriate skills. These may include institutional or noninstitutional
services or human interactions that improve people’s learning to develop digital literacy
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and comfort using technology. For this study, types of learning were based on Mezirow’s
(2009) theory of transformative learning, including (a) instrumental learning, which is
technical and involves problem solving, and (b) communicative learning, which is
observational and interpretive and involves self-reflection.
I selected a basic qualitative design for this study because I aimed to interpret the
experiences and perceptions of the participants (see Merriam & Tisdell, 2016), who were
college students over 50 years of age becoming digitally literate. I did not select a
quantitative approach because such research designs focus on measuring statistically
significant relationships identified within a large sample to apply findings to a broader
population (see Maxwell, 2010). In addition, quantitative research includes singular and
objective factors to present unbiased data, with the research process often beginning with
a hypothesis and resulting in a cause-and-effect observation (Mertler & Reinhart, 2017;
Rottman & Hastie, 2014). Because the intent of the current study was not to measure or
collect numerical data that might suggest a cause-effect relationship or apply to a larger
population, a quantitative approach was not appropriate.
A basic qualitative study was appropriate for this study for several reasons. First,
Merriam and Tisdell (2016) described basic qualitative research as philosophically
derived from constructionism, phenomenology, and symbolic interaction. Researchers
use this approach when they are interested in “(1) how people interpret their experiences,
(2) how they construct their worlds, and (3) what meaning they attribute to their
experiences. The overall purpose is to understand how people make sense of their lives
and their experiences” (Merriam & Tisdell, 2016, p. 23). Second, bridging a gap in the
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literature is one function of qualitative research, bringing about added avenues for
exploration (Corbin & Strauss, 2014). Third, qualitative research provides an
understanding of individual experiences to better capture people’s experiences by using
deep descriptions of the meaning of experience (Hays & Wood, 2011).
A qualitative method provides a framework to explore a phenomenon from a
range of different perspectives (Lauckner et al., 2015). According to Yin (2014),
qualitative methodologies can support theories on human factors and human behaviors.
These phenomena include (a) how people experience aspects of their lives, (b) how
individuals or groups behave, (c) how organizations function, and (d) how interactions
shape relationships (Denzin & Lincoln, 2013; Teherani et al., 2015). According to
Merriam and Tisdell (2016), qualitative researchers use data collection methods “such as
interviews, focus groups, observations, and analysis of documents or artifacts” (pp. 52–
53).
Percy et al. (2015) described the basic qualitative approach as instrumental in
studying attitudes and feelings in which data come from individuals’ perceptions. I aimed
to gather personal accounts of learners over 50 through individual interviews and two
focus group sessions. Using a discovery process within the focus group setting, I learned
about the students’ unique needs, supports, and types of learning required for them to
acquire digital literacy. Through individual interviews followed by small focus groups, I
questioned, probed, and clarified the experiences of all participants. Together with
interviews and focus groups, small sample sizes are suitable for qualitative data
collection (G. Guest et al., 2017).
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The narrative qualitative design is practical when combining information from
various approaches, whether the researcher collects data from a one-on-one interview or
by survey (S. J. Taylor et al., 2015). Within a narrative design, the researcher develops a
clear understanding of the context of the individual’s life (Merriam & Tisdell, 2016). I
rejected a narrative qualitative design because I wanted to identify the experiences and
ideas that influenced a specific population at a particular stage.
I used a basic qualitative approach that helped me identify emerging perceptions
and interpretations that affected a distinctive unit of people at a point in time (see S. J.
Taylor et al., 2015). In this study, I gathered descriptions of firsthand experiences and
perceptions from a group of students over the age of 50. Although the basic qualitative
and phenomenological methodologies are similar, the basic qualitative approach focuses
on the varied lived experiences of participants (Merriam & Tisdell, 2016). A
phenomenological approach is used to identify the essence or defining characteristic of a
shared experience (Patton, 2002; Percy et al., 2015). Exploration in a basic qualitative
study includes personal experiences and gaining in-depth descriptive reflections
(Creswell & Poth, 2019; Merriam & Tisdell, 2016), which aligned with my research
aims.
I rejected grounded theory and case study designs. Researchers select grounded
theory to form theories, which was not appropriate for my research. Likewise, a case
study design helps the researcher accurately depict a phenomenon within a bounded real-
life setting (Creswell & Poth, 2019; Merriam & Tisdell, 2016). This design did not align
with my intent to understand the perceptions of older college students who attended
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HEIs, and the study did not have a shared context. The research questions were best
answered using a basic qualitative design, an exploratory approach intended to seek
information about a specific phenomenon experienced by individuals (see Bordens &
Abbott, 2011).
Role of the Researcher
In qualitative research, the role of the researcher is that of the data collection
instrument that prepares and obtains data from surveys, interviews, and focus groups
(Merriam & Tisdell, 2016). Qualitative research requires identifying personal values,
assumptions, and biases at the start of a study. I am a training and learning facilitator of
online learning for students and an adjunct instructor in my professional work. I had seen
the value of supporting students as they develop digital literacy skills and the importance
of facilitating a way to help them apply technology skills to produce fluid
communication. However, I also saw a substantial barrier for older adult learners using
the internet as a wide-ranging lack of digital literacy. They seek to acquire digital literacy
skills within their college environments. Thus, I had a bias in favor of technology
supports for all learners, regardless of age.
My responsibility as a facilitator and trainer made me aware of the substantial
barriers older adult learners encounter when using new technologies. Therefore, I brought
certain biases to this study. I made every attempt to safeguard my neutrality by mediating
my potential biases. Because I used two focus group sessions to explore participants’
beliefs, their ability to communicate accurately and honestly may have provided a
limitation because they may have been less willing to disclose their level of digital
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literacy. Documenting my potential biases minimized threats to the validity of the study. I
consistently monitored potential bias using reflexive journaling (see Merriam & Tisdell,
2016) to reduce possible negative consequences to the research outcomes.
As an educational technology coordinator, it is my job to train faculty and staff to
integrate technology into their classrooms, improve student achievement, and ensure that
students meet and exceed common core standards. I also identify appropriate technology
platforms that support an effective learning environment. In addition, I am an adjunct
instructor and use the technology that I train to facilitate learning. This may have
introduced potential bias in my study. Therefore, I documented and reflected on any
tendencies to misinterpret data that could have affected the results of this study, as
suggested by Merriam and Tisdell (2016). Ambiguity and evidence regarding any
presumptions within data collection is an extreme concern for researchers (Birt et al.,
2016).
I approached this study with an open mind by taking note of all evidence
collected. In my professional role as educational technology coordinator, I have firsthand
knowledge of training, modeling, and integrating technology standards that meet and
surpass the common core standards. Additionally, I have professional relationships
throughout the educational system. I made sure that these professional relationships did
not impact the research by consistently monitoring the potential of bias using reflexive
journaling (see Merriam & Tisdell, 2016). This reduced possible negative consequences
to the study’s outcomes.
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To avoid any personal bias, I utilized specific strategies. First, I did not collect
any data from my current place of employment. Instead, I collected data from other
institutions that offered online campus-based instruction throughout the United States.
Therefore, the chances were minimal that I would know any of the study participants.
Plans to avoid bias included keeping a reflexive journal on what was taking place during
the study; member checking; admitting my perceptions; and being aware of participants’
feedback, opinions, and assumptions (see Merriam & Tisdell, 2016).
Methodology
In this section, I explain the process of implementing the study. Once I secured
IRB approval (11-11-20-0535898), I began the research process. The following topics are
covered in this section: participant selection logic and procedures for recruitment,
participation, and data collection.
Participant Selection Logic
The population for this study was college students over the age of 50 enrolled in
traditional 4-year HEIs across the United States. I invited participants to take part in
individual interviews followed by two focus group sessions. I used a snowball and
convenience sample of 12 students over the age of 50 because it allowed participants to
recruit additional participants (see Naderifar et al., 2017). Convenience sampling also
allowed me to focus on specific qualities of the study’s population to answer the research
questions (see Etikan et al., 2016). Convenience sampling requires that participants meet
inclusion criteria and is used when a “diverse sample is necessary or the opinion of
experts in a particular field is the topic of interest” (Martínez-Mesa et al., 2016, p. 328).
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For the current study, the inclusion criteria required individuals to be (a) at least 50 years
of age, (b) currently enrolled in a traditional 4-year higher education institution, and (c)
voluntarily participating in the study. I assured the participants that they would have
confidentiality, that their participation was voluntary, and that they could withdraw from
the study at any time.
The number of participants was based on recommendations for qualitative data
saturation for interviews and focus groups. The number of participants for individual
interviews was 12, following recommendations to obtain rich and deep data (see Creswell
& Poth, 2019; Maxwell, 2005; Patton, 2002). For focus groups, Creswell and Poth (2019)
recommend the use of five to 25 participants. In a study comparable to mine, Menzies et
al. (2017) held focus groups to explore students’ experiences and opinions regarding their
use of Facebook, with a total of 11 participants divided between two focus groups. I
interviewed 12 participants and conducted two focus groups consisting of three
interviewed participants each.
The concept of saturation in qualitative research indicates the basis for adequate
sampling related to developing a theoretical category in the data analysis process (Sim et
al., 2018). Malterud et al. (2016) proposed the concept of information power to determine
sample size in qualitative studies. When information-rich data is elicited from a smaller
number of participants, fewer participants are needed. Within this study, the ability to
reach saturation using guidelines provided by Creswell (2019) determined the sample
size to be sufficient to address the research questions. The participants’ detailed
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descriptions gathered from the interviews and the focus groups thus supplied information
power (Malterud et al., 2016).
Procedures for Recruitment and Participation
I recruited college students over the age of 50 through Walden University’s
participant pool and Facebook. In addition, I sent potential participants a solicitation
message. Once I had a pool of potential participants that self-identified their full-time
enrollment status of 12 or more credit hours, I sent everyone a consent form via email. I
asked each participant to reply to this message with the statement “I consent.” I
downloaded these messages and stored them on a secure disc as documentation of
consent. Once I received permission from participants, I created a document with their
names, contact information, and assigned pseudonyms (Participant 1, 2, 3, etc.). From
that point forward, I only used assigned numbers on any transcription, data analysis, or
report. I kept the original consent forms and list of participants on a secured computer. I
then repeated the recruitment process if I had not reached data saturation. Finally, after
data collection, I notified participants that this part of the research was completed and
asked them if they would like a summary of the research findings.
Data Collection
I began with individual interviews, which were conducted over 3 weeks. To
schedule interviews, I used an online scheduling tool to identify possible dates and times.
Next, I confirmed with participants a time and date for the interview to take place via
Zoom.
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The interview, focus group questions, and script I used as a guide are all detailed
in Appendix A. Additional data collection that followed the interview process were
developed by aligning interview questions with research questions to ascertain what had
not been answered. Interview analysis was used to create focus group questions, which I
used to direct the focus group sessions.
I began each interview session by describing an overview of the study. The
participants understood their participation was voluntary, and they could withdraw from
the study at any time, as per the consent form. I also provided my contact information and
ensured the participants understood their contribution to the study would be anonymous
and confidential. Recorded interviews lasted between 30 and 45 minutes. I explained to
each participant that after transcribing the recordings, I would send a summary of the
session as a form of member checking (Birt et al., 2016). At the end of the session, I
asked for any final comments or questions. I asked that they respond within one week
with any corrections, comments, revisions, or additions.
After analyzing all interview data, I identified patterns, areas of consensus,
anything that was unique, and areas that required elaboration or clarification to provide a
foundation for the two focus group sessions (Barbour, 2007; Merriam & Tisdell, 2016). A
focus group is appropriate for college students over the age of 50 because they can
communicate their views, listen to what their peers believe, and think about their
perceptions while exchanges occur (Merriam & Tisdell, 2016). In addition, because this
population appreciates social learning and interaction (Dauenhauer et al., 2016; Khalaf et
al., 2018; Seo et al., 2019), they might have revealed more and been more reflective
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about their experiences when they heard their peers. A limitation of focus groups may
include a possibility of conformity or a reluctance to disagree with other group members.
This was not the case in this study
I used an online scheduling tool to identify when at least a total of six participants
could participate in one of two online focus group sessions. I then notified participants of
the date and time and shared login information to an online Zoom meeting room. I
conducted each focus group session with three participants using the general interview
protocol and questions derived from the interview data analysis. Each participant signed
into every meeting with their assigned participant number. Focus group members wanted
their cameras on so I video-recorded the focus group sessions and used their assigned
number as their participant’s name to ensure anonymity, and then transcribed the entire
session verbatim. I kept the interview and focus group recordings and transcriptions in a
secure file on an encrypted password-protected external hard drive to ensure
confidentiality. I stored this hard drive in a locked file cabinet when not in use.
Focus group sessions were conversational and these questions from Appendix A
guided each session. Here are the initial questions that were used to guide the discussion:
• In general, what has been the most challenging thing about returning to
school?
• How has using technology shaped the way you think about how courses are
taught, how you interact in class, or just how you think about what course you
may take next?
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• In your interviews, each of you discussed your level of digital literacy and
how it compared to your peers. How did you realize that you may not have the
same level of skills as your younger peers?
• What type of support do you feel you needed at the beginning of your college
of experience to ease you into using the required technology platform for
courses?
At the end of each focus group session, I informed the participants that follow-up
questions would be sent via email and a summary of the session for their corrections,
confirmation, and corroboration of its accuracy if needed. I sent each participant a
summary of the session. This form of member checking allowed me to verify my
understanding and accuracy (Nowell et al., 2017).
Data Analysis
Data analysis is an iterative process that the researcher begins after collecting
each data set and builds upon ongoing analysis as the researcher returns to prior analyses
(Merriam & Tisdell, 2016). I used the software NVivo for the organization of the data
during the analysis process. After analyzing individual interview transcriptions, I
reviewed previous analyses to ensure I did not overlook a pattern that may have emerged
throughout the data set. I began this process with the precodes developed from the core
constructs of my conceptual framework models, see Table 1. I started with the interview
data and used this analysis process to form a basis for the focus group sessions. Next, I
identified and applied emergent codes supported by specific statements from interviews
and the focus group sessions for both data sets. I then developed categories to form the
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basis of themes. Finally, I created themes based on keywords and phrases from
participants. A more detailed description follows.
I followed a detailed process of data analysis as outlined by Merriam and Tisdell
(2016). First, I entered transcriptions of interviews and my field notes into NVivo, and, as
I reviewed the data, I applied precodes to each data set. If the precodes did not reflect the
patterns I detected, I used open coding (Merriam & Tisdell, 2016) and created new codes.
I kept a running list of all emergent codes and descriptions of their meaning. Once I
analyzed all interview data, I identified areas for the follow-up focus group sessions.
Finally, I reviewed the focus group sessions and interview data to determine
patterns emerging across data sets. As I did with interview data, I entered all focus group
data into NVivo and applied the same process as with the interviews, beginning with
precodes and the emergent codes I created. If new patterns emerged, I added them to my
running list of codes.
Once I analyzed all collected data, I looked for more significant categories of
meaning that formed themes (Merriam & Tisdell, 2016). As I grouped the coded material
into tentative themes, I wrote a description of the meaning of that theme. I also referred to
my research questions and noted how each theme addressed the research questions and
how they centered on the purpose of this study. If discrepant cases were evident, I
determined if they revealed an insight into the patterns I identified or if they were
exceptions. These are described in Chapter 4. Additionally, while constructing themes, I
considered my biases to ensure that my thoughts did not skew the data collection process.
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Table 1
Research Questions, Interview Questions, Adult Learning, Digital Literacy, Initial
Precodes
RQ Interview question Adult learning Digital literacy Initial precodes
RQ2 Thinking about your technological
abilities, are your abilities consistent
with your peers, higher than your
peers, or lower than your peers?
Instrumental &
Communicative
Interdependence
& Social Factors
Learn from
peers
Critical
reflection
RQ2 What software or computer
programs do you most often utilize
in your college experience?
Instrumental &
Communicative
Interdependence Prior experience
RQ2 In what ways does your use of
technology help or hinder your
learning?
Instrumental &
Communicative
Problem solving
Critical
reflection
Work
experience
RQ1
RQ2
What is your primary use of
technology in your coursework?
Instrumental &
Communicative
Problem solving
Relevance
Experience
RQ 1
RQ2
What resources do you use when
you need assistance with technology
in your college experience?
Instrumental &
Communicative
Interdependence
& Social Factors
Self-directed
learning
Peers
Instructors
Family
members
RQ 2 Describe which technologies have
presented the greatest challenge in
your college experience?
Instrumental &
Communicative
Relevance
Self-awareness
Problem solving
RQ1
RQ2
Describe the technological training
provided by your institution if any.
Was it helpful? If yes, why? If not,
why?
Instrumental &
Communicative
Interdependence Self-awareness
Problem solving
RQ 1 In general, how do you prefer to
learn about new technologies?
Communicative Critical
reflection
Problem solving
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Issues of Trustworthiness
Measures for validity and reliability of qualitative research include factors known
as credibility, transferability, dependability, and confirmability (Korstjens & Moser,
2018). In this section, I detail the actions I took related to these factors to ensure
trustworthiness.
Credibility
Credibility establishes whether the research findings represent credible and
reasonable information obtained from the participants and represents a correct
interpretation of the participants’ views (Lincoln & Guba, 2011; Nowell et al., 2017).
Within this study, I established credibility by making sure my research process was
replicable and transparent. To ensure credibility, I used member-checking to confirm that
participants agreed with my interpretation of what they reported. Member checking
requires that participants verify their interview accountings’ accuracy to help ensure the
trustworthiness of the data (Candela, 2019). In addition, member checking permitted the
participants to support the findings and verify the research through a summary of the
interaction (Yin, 2014). Through validating information gathered within supporting data,
I improved the accuracy of the findings.
Ortlipp (2008) stated that reflective practices are an acknowledged part of
research design development. As the researcher, I documented the research process
through field notes recorded in a journal. I validated every step of the research planning,
from the study’s design, the sampling processes, data acquisition and analysis, and results
and conclusions to ensure transparency, rigor, and consistency (Leung, 2015).
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I used member checks to establish trustworthiness (Elo et al., 2014). Member
checking is a quality control process used to improve the accuracy, credibility, and
validity of what I recorded during the two focus group sessions (Harvey, 2015). In
addition, member checking provided an opportunity for self-reflection, enhancement of
the findings and shifted the power from the researcher to the participants (Richards,
2003). For this study, I used member checking after each focus group session. I created a
summary transcript of each focus group session and sent it to each participant, asking
them to review, edit, add, or correct any statement from their perspective. While each
person may vary in their recollection, I was more likely to eliminate my own bias or
misinterpretation of what participants stated by synthesizing all feedback.
Transferability
Transferability is the degree to which the results of qualitative research apply to
other contexts or settings (Leung, 2015). Transferability is challenging in qualitative
research because of small sample sizes and the nature of the research process or limited
population. While findings may have limited transferability, I followed rigorous steps to
make sure the results were accurate. I interacted directly with the participants, probing to
uncover details about their experiences and perceptions, establishing an accessible online
meeting environment, and scheduling contact convenient for the participants.
Dependability
Dependability showed the research processes – data collection, analysis, and
findings - as consistent and potentially repeatable (Merriam & Tisdell, 2016). I verified
results and conclusions as consistent with the data collected, evidenced through a data
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audit trail in my documented field notes. Dependability infers if other researchers were to
examine my research data, they would arrive at a similar analysis (Korstjens & Moser,
2018). I included an audit trail of the research method, documenting what worked and
what did not.
Confirmability
Merriam and Tisdell (2016) noted that a qualitative researcher admits their bias,
but the approach must be based on confirmable practices, studies, and assumptions.
Confirmability is the degree to which other researchers could confirm the research
study’s findings (Nowell et al., 2017). Confirmability establishes that data and
interpretations of the results are not figments of the inquirer’s imagination but are derived
from the data. My use of reflexivity included critical self-reflection about myself as the
researcher – examining biases, preferences, preconceptions, and my relationships with
the participants. It revealed how those researcher-participant relationships might have
affected the participants’ answers to the interview questions (Palaganas et al., 2017). I
recognized my biases as I reflected after each interview and coding session and recorded
any biases in my reflective journal.
My reflective research journal notated contemplations of the focus group sessions
as I reported my thoughts, feelings, potential bias, and observations. I also took field
notes during the focus group sessions to maintain confirmability. In addition, I followed
suggestions received from my dissertation committee.
To achieve excellence in this study, I followed the guidelines for quality research
standards put forth by Walden University, including criteria, process, and documentation
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of the interviews and focus groups. Reciprocity between researchers and participants was
an essential part of this qualitative research (Palaganas et al., 2017; Sivell et al., 2019).
To obtain content validity, I addressed the needs and supports required for the older adult
participants. According to Yin (2014), construct validity is the accuracy in which a case
study’s outcome mirrors the concepts that I addressed through this research. The
researcher’s analyses of the data from interviews and the focus groups, the member
checks, and the reflective journal provided validity. The findings from data collection
substantiated a conclusion and recommendations for future research discoveries.
Ethical Procedures
This study followed the rules required by Walden University’s internal review
board (IRB). In addition, participants received a copy of the informed consent document,
which included information about risks, possible benefits of the study findings, the
voluntary nature of their participation, and the ability to cease participation at any time.
At the beginning of all interviews and each focus group, I described in detail to
the participants of this study the specific ways the study was designed to prevent their
distress or emotional harm. Further, I explained that if any of the participants wished to
stop, pause, or leave the study, or if they did not want to continue to participate for any
reason, they would have the opportunity to depart at any time. Participant withdrawal was
minimal as I ensured that all information for participation was clear, and that
participation was voluntary. I obtained consent to record the focus group participant
discussions through email.
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I informed participants that all information was confidential. I kept all digital
information in a password-protected file only available to me in a digitally secured file on
a separate drive accessed through a password-protected computer and on a USB drive
backup copy locked in a filing cabinet in my home. I will shred all paper records
produced during data analysis after the required 5-year period. Additionally, I will keep
all consent forms digitally in a secure file on a separate drive accessed through a
password protected computer and on a USB drive backup copy locked in a filing cabinet
in my home. There will be no other access to the data, and I will delete all files and
destroy them after 5 years.
Summary
This chapter presented the basic qualitative research design I used to explore how
college students over 50 years of age interpret their experiences and perceptions of
becoming digitally literate. After receiving Walden University IRB approval, I recruited
U.S. college students through the Walden University participant pool and social media,
such as Facebook and LinkedIn. Data collection occurred through 12 individual
interviews and two focus group sessions. After data collection, I summarized each
session and sent transcriptions to the participants to member check. The analysis of the
data took place using precodes and emergent coding, after which categorical analysis
aided in identifying themes. Once I determined patterns from the interviews, I
constructed probes to conduct two focus group sessions. After the data were collected
from the focus group sessions, I repeated the data analysis process and selected themes
applied to all data sets.
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Chapter 4: Results
The purpose of this basic qualitative study was to explore and better understand
the supports and types of learning required for digital literacy development for college
students over 50 who attend traditional 4-year institutions. The research questions for this
study ensured that the purpose of my research was at the forefront. The research
questions were the following:
RQ1: How do college students over 50 years of age describe their learning as they
develop digital literacy skills?
RQ2: What types of support do college students over 50 years of age report most
helpful in their development of digital literacy?
This chapter includes a description of the setting, participant demographics, data
collection, data analysis, evidence of trustworthiness, and results.
Setting
Twelve undergraduate college students over the age of 50 who attended six
different traditional 4-year institutions around the United States participated in this study.
Due to the restrictions of the COVID-19 pandemic, I conducted Zoom sessions for
individual interviews and focus groups. Each student participated in a 30- to 45-minute
recorded online interview. Because participants resided in different time zones,
scheduling focus group dates and times was a challenge. For participant convenience, I
conducted two focus groups with six of the 12 interviewed participants based on their
schedules.
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Demographics
The 12 study participants were college students over the age of 50 who attended a
4-year institution. Seven participants were campus based, and five participants were from
online institutions. At the time of the study, the campus-based students were taking
online courses due to the pandemic. Each participant indicated that a learning
management system (LMS) was required to complete and be successful in all
coursework. The students’ self-assessed technological ability ranged from lower than to
consistent with to higher than their peers. To ensure confidentiality for this study, I
omitted all identifying information about each student, and I assigned each participant a
number in the order they were interviewed. Table 2 summarizes the demographics of the
participants and their self-rated level of digital literacy self-rating participants.
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Table 2
Participant Demographics and Digital Literacy Self-Ratings
Participant Gender Type of institution Classification Location Digital literacy self-
rating
P1 Female Campus-Based Sophomore Southwest U.S. Higher than Peers
P2 Female Campus-Based Junior Eastern U.S. Lower Than Peers
P3 Male Campus-Based Junior Southern U.S. Lower than Peers
P4 Female Campus-Based Junior Southwest U.S. Lower than Peers
P5 Female Online Junior Southwest U.S. Lower than Peers
P6 Male Online Sophomore Southwest U.S. Lower than Peers
P7 Female Online Sophomore Eastern U. S Lower than Peers
P8 Female Online Sophomore Eastern U.S. Consistent with Peers
P9 Female Online Senior Eastern U.S. Lower than Peers
P10 Female Campus-Based Senior Eastern U.S. Lower than Peers
P11 Female Campus-Based Junior Southern U.S. Lower than Peers
P12 Female Campus-Based Junior Eastern U.S. Lower than Peers
Note. Digital literacy self-ratings indicated how participants ranked themselves as compared to peers.
Higher than peers: Advanced/Developed/Proficient/Well-Informed. Consistent with peers:
Accurate/Steady/On-Pace. Lower than peers: Needs Assistance/More Practice/Unacquainted/Ill Informed.
Participant 1 was a sophomore who returned to school after beginning her
undergraduate degree in 1978. Her digital self-rating was higher than my peers. She
stated that she had built computers since the late 1970s and she felt comfortable with
navigating her institution’s LMS.
Participant 2 was a campus-based junior. She gave herself a digital self-rating of
lower than my peers. She began her degree because she believed a professional degree
would allow her to make more money in her field. This was her third year in her
program, and although she had been a student at her institution for 3 years, she still felt
uneasy navigating her school’s LMS to communicate with her instructors and to submit
assignments.
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Participant 3 was a junior who attended campus-based courses with a digital self-
rating of lower than my peers. He was employed in the field of education and enrolled in
the K–12 specialization as a counselor. In his work he counseled students about what
they needed to know for the transition from high school to college, but he did not feel
comfortable or prepared with his use of digital technology. He saw the importance of
helping students prepare to use technology on different levels.
Participant 4 was a junior who returned to school due to a furlough in
employment and assessed her digital literacy as lower than my peers. She stated that
although returning to school was not her plan, she was happy to have the opportunity to
go back, and her furlough offered the chance to do that. During the interview she often
reflected on how college differed from when she was there years ago and that the change
with technology was overwhelming but that she was up to the challenge. During her first
college experience, she recalled seldom using a typewriter to submit a term paper but
now everything was submitted and done through technology.
Participant 5 was a junior at an online institution whose biggest challenge was a
language barrier, and she rated her digital literacy as lower than my peers. This language
barrier caused her to reach out to her grandchildren often, which she said made her feel
like a burden to them because they had their own schoolwork to do. She thought being an
online student and communicating in English so much would be a benefit. However,
because English was not her first language and the LMS was not user-friendly, she
struggled. She described the LMS as disruptive to her work because of updates and
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outages, yet when she reached out to support offices or personnel for assistance, she felt
her concerns were not understood.
Participant 6 was a sophomore attending an online institution. He felt that the
process of submitting assignments, taking exams, and using the LMS was very time-
consuming and overwhelming. He felt that being able to obtain a degree online was very
helpful due to his personal and professional life. His digital literacy self-rating of lower
than my peers stemmed from what he perceived was the lack of training from his
institution on how to use the system. He thought the institution assumed that students
were already comfortable using an LMS to complete and submit assignments, but for him
that was not the case.
Participant 7 was a sophomore who attended an online institution with a digital
self-rating of lower than my peers. She relied on her grandchildren a great deal for
support and as a resource with using her institution’s LMS to complete assignments. She
repeatedly mentioned that she did not feel like she would be successful until she got a
grasp and started to feel comfortable using the required technology. She also stated that
she always tried to “go around” technology because she only used it when necessary. Of
course, she said she used a cellphone and played games online, but that was the extent of
her technology use. She repeatedly said there was no way around it now.
Participant 8 was a sophomore who attended an online institution. She gave
herself a digital-self rating of consistent with my peers because she was able to interact
with them in her discussions and felt comfortable using her institution’s LMS. At first,
she was overwhelmed, but at the beginning of her coursework her school required
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students to take a course devoted to learning how to navigate the school’s LMS. This was
very helpful because she was able to get most of her questions and concerns answered
about a system that she was not familiar with. She also talked about how completing
everything through an online platform was different from anything she had to do in the
past. She recognized why it was helpful because she was in class with students from
around the world, which she found fascinating.
Participant 9 was a senior who attended an online institution. Although she gave
herself a lower than my peers digital literacy rating, she was very excited about her
efforts in getting ready to complete her degree. Her rating was based on her perception
that she did not seem to be as digitally proficient as other students in her courses who
seemed to understand things from the beginning. She also stated that she felt comfortable
navigating her institution’s LMS. However, it took her a while to feel confident using it.
Participant 10 was a senior who attended a campus-based institution. She rated
her digital literacy as lower than my peers because she realized that she was behind with
her ease of using technology and her younger counterparts seemed to use the LMS with
ease. She stated that she talked to several of her instructors about this and that they
agreed that because younger students start using similar systems before they come to
college, it may be easier for them to transition faster than someone who has not used this
kind of system at all.
Participant 11 was a junior who attended a campus-based institution. She said the
pandemic showed her how uncomfortable she was using technology, and she rated
herself lower than my peers. Because everything was moved online, she felt that
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communication was not the same with her instructors or her peers. She also preferred
taking her exams in class and not online because she had a few computer issues that made
things difficult. Taking courses in class was her preference. She could not wait until she
could get back into the campus-based classroom.
Participant 12 was a student junior who attended a campus-based institution. Her
digital-self rating of lower than my peers came from the fact that she felt she needed
more technological support on a one-on-one basis. She stated she was more of a tactile
learner and that she learned best by doing activities repetitively to get comfortable.
Participant 12 talked about using YouTube for assistance with issues with technology.
Due to the pandemic, her classes were switched from on campus to online, so that made
things a little more difficult for her because she preferred more hands-on instruction. For
example, she had to submit an assignment using an online recording system rather than
submitting a written assignment.
All 12 participants were aware of their digital skills and used a range of strategies
for learning and using required technologies. Participants all were eager to discuss their
experiences and provided a rich and thick set of data.
Data Collection
Data collection took place over 6 months. I recruited participants from the
Walden University participant pool (a university resource for staff and students to recruit
participants) and a Facebook ad that reached students across the United States. These two
approaches allowed me to recruit 12 interview participants, of whom six agreed to
participate in a follow-up focus group.
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Data collection involved interviews with 12 participants through Zoom and two
focus groups with three participants each, also through Zoom. Focus Group 1 consisted
of Participants 2, 3, and 6. Focus Group 2 consisted of Participants 5, 9, and 11. With
each participant’s permission, I made an audio recording of each interview available
through the Zoom app. After each interview, I forwarded recordings to Rev, an online
transcription service that transcribed the interview and focus group recordings. Using an
outside transcription service was not initially planned, but using it gave me more time to
focus on data collection and analysis. Each focus group participant received an email
with a summary of the session.
The information gathered during the focus group not only added to my
understanding of students’ experiences but also confirmed data collected through the
interviews. The focus groups allowed me to capture participants’ perceptions in a way
that the one-on-one interviews did not. I elicited elaboration from respondents’
comments, probing more deeply about their attitudes, feelings, beliefs, experiences, and
reactions. When sharing their responses in a group setting, participants may have refined
or clarified their perceptions through the interactive dialogue (see Merriam & Tisdell,
2016). Moreover, the focus groups helped me confirm interview data by allowing
participants to think aloud, elaborate, and affirm each other’s reports.
Data Analysis
The purpose of data analysis is for the researcher to produce a clear meaning for
the data by “consolidating, minimizing, and interpreting” the study’s findings (Merriam
& Tisdell, 2016, p. 202). The data analysis was active and continuous as described in
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detail in this section. After data collection, data analysis followed as informed by
Merriam (2009) using multiple levels of coding. In the first level, I analyzed interviews
and then the focus groups using the precodes (see Table 3), and in the second level, I
identified emergent codes. I discovered and noted which precodes applied to the data and
utilized line-by-line initial coding to assess the data that did not fit with the pre-
established codes to find emergent codes and common repeating patterns in the new
qualitative data.
1. Using the precodes, I coded interviews as they were transcribed (see Table 3),
reading and re-reading the interviews after each transcript was coded. Data
analysis involved applying precodes, and then identifying emergent codes,
and finally articulating themes (Merriam & Tisdell, 2016; Saldaña, 2016)
related to participants’ descriptions and specific supports they expressed as
being needed while developing digital literacy.
2. I analyzed each focus group transcript and determined how thematically they
were related, after which I added to the existing themes. I made sure to
consider differences between interviews and focus group responses.
3. I gathered data until I reached saturation, at which point no new patterns
arose.
After analyzing the interview data, I discovered only two outliers in the form of
participants who did not fit the general pattern in their self-assessment of digital literacy.
The codes from the interviews guided the initial data analysis. I used the same codes from
the interviews to build that data analysis from the focus groups. A review of the focus
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group transcripts revealed no new codes or any outliers. At the start of focus groups,
participants wanted to discuss their ages (between 55 to 60 years of age) and how they
felt age played a role in their adoption and impressions of technology. When I analyzed
the data, I used both the original precodes and the emergent codes that I devised from
data analysis.
Focus group sessions built on the patterns from the interviews. No new codes
were identified during analysis of the focus group transcripts. When I went back to look
at the interview codes, the themes were the same. As a result, the focus group sessions
corroborated the interview findings.
Table 3
Precodes, Codes, and Frameworks: Relationships to Research Questions
Precode
Emergent code
Framework
RQ
Critical reflection,
Prior experience
Reflections of the past
Tech savvy/tech novice
Reasoning process
Making meaning of an
experience
Transformative learning
RQ1
RQ1
Support/resources
Social factors
24/7 support, training,
learning platform,
learning style, step-by-
step support, hands-on
learner, tactile learner
Learning outcomes
Sustainable development
Digital competence digital
literacy
RQ2
RQ2
RQ2
RQ2
Problem solving
Essential technology
Enhanced digital workflow
Transformative learning &
digital literacy
RQ1
Evidence of Trustworthiness
Quality criteria for qualitative research include the trustworthiness of qualitative
research’s validity and reliability. Therefore, the confirmability of data from the study
will indicate trustworthiness through its credibility. Furthermore, the analysis methods
must present results in enough detail to allow the reader to determine whether the process
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is credible (Korstjens & Moser, 2018). I followed research procedures in this study by
observing the overall protocols for conducting interviews and guiding the focus groups.
Each interview and focus group participant were informed about confidentiality and
protection of their identity at the start of each interview and focus group.
Credibility
I used member-checking to ensure that participants confirmed my interpretation
of what they reported to ensure credibility. Member checking requires that participants
verify their interview accountings’ accuracy to help ensure the trustworthiness of the data
(Candela, 2019). After an interview, I sent each participant a summary of our
conversation and asked for corrections. The participant member checking improved the
accuracy of the findings. I documented the research process through field notes recorded
in a journal to further assure credibility. I took notes all throughout, and if something did
not work, I rephrased it and identified patterns from my journal.
Transferability
This study had a small sample, which restricts the degree to which the findings
can be applied to a broader population. However, I sought to examine the viewpoints of a
diverse group of nontraditional students by seeking study participants nationwide from
around the United States. The Facebook advertisement reached students in the southern,
eastern, and southwestern parts of the United States. While findings may have limited
transferability, I followed rigorous steps to make sure the results were accurate. The steps
included interacting directly with the participants for as much time needed to establish
trust and rapport, probing to uncover details about their experiences and perceptions,
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establishing an accessible online meeting environment, and scheduling contact
convenient for the participants. The findings illustrate the types of experiences that might
be shared by students of a similar age pursuing undergraduate degrees and thus could
inform policy and practice at institutions that enroll such students.
Dependability
Dependability infers that if other researchers were to analyze my research data,
they would arrive at a similar analysis (Korstjens & Moser, 2018). To confirm
dependability, interviews and focus group sessions were recorded through the Zoom
application and transcribed by Rev transcription service. Each transcription included a
summary, and both the summary and transcriptions were checked verbatim for accuracy.
Also, I used member checking during the data collection process and kept detailed
records and reports of the study’s findings in a secure location to ensure consistency and
integrity throughout the study (Merriam & Tisdell, 2016). I verified results and
conclusions as consistent with the data collected, evidenced through a data audit trail in
my documented field notes.
Confirmability
Confirmability is the degree to which other researchers could confirm the research
study’s findings (Nowell et al., 2017). Confirmability establishes that data and
interpretations of the results are not figments of the researcher’s imagination but are
derived from the data. My use of reflexivity included critical self-reflection about myself
as the researcher – examining biases, preferences, preconceptions, and my relationships
with the participants. I recognized my biases as I reflected after each interview and
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overview of each transcription summary and recorded any biases in my reflective journal.
My journal entries were the basis for anything I learned that would improve data
collection. For example, if I used a term a participant did not understand, in subsequent
interviews I made sure to use different language. I also noted in my journal when an
interview was rushed or incomplete. I then scheduled more time for participants to give
feedback if something was not clear. After more reflection of my journal notes, I took the
emergent codes and precodes that came from those journal notes and put those codes on
post-it notes, and then put them on my wall so that I could see them begin to show me
themes and patterns.
To achieve excellence in this study, I followed the Walden University guidelines
for quality research standards, including criteria for inclusion, process of data collection,
and documentation of the interviews and focus groups. The relationship between
researchers and participants was an essential part of this qualitative research to elicit the
deep and rich descriptions from participants (Palaganas et al., 2017; Sivell et al., 2019).
To obtain content validity, I addressed the needs and supports required for the older adult
participants making sure I allowed sufficient time, answered all their questions, and
probed to determine anything they might not freely disclose. For both research questions,
tables 4 and 5 list the final codes and categorize the themes.
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Table 4
Final Codes, Categories, Themes, and Examples for Research Question 1
Final code Category Theme Example
Critical
Reflection,
Prior
Experience
Reflections of the
past
Tech savvy/tech
Novice
Self-Assessment of digital literacy
describes the participants’ ability to
understand the technology and apply
digital resources to be successful in
their work.
I keep trying to go back to
the way it was 20 years ago
sometimes. It’s like why is
this button not here, then I
realize, “Wait a minute. 20
years ago, was not 1980.”
(P1)
Technology
Environment
Making meaning of
an experience,
Training, Learning
Platform, Learning
Style, Step-By-Step
Support, Hands-On
Learner, Tactile
Benefits of technology learning
Outcomes involve the essential and
significant learning that has been
achieved by using technology.
Organizational support needs for
skill development is an environment
in which digital tools and resources
are used to enhance learning,
communicate utilizing critical
thinking abilities, and assess material
so that the learner may internalize
concepts and acquire authentic skills.
I don’t know, like a, a town
hall or Excel lunch or a
PowerPoint lunch that I could
go to, maybe just to kind of,
you know, see how to do
some of these things. (P5)
Problem
Solving
Essential Technology
Critical Thinking -
Use of critical
thinking skills to
evaluate digital
information.
Enhanced digital learning workflow
is evident by the confident use of
technology for information,
communications, and problem-
solving
I prefer to be told about it,
and then immersed and said,
“Okay. This is how you do
it.” Um, you know, kind of
an overview, but then a
hands-on learning. I guess
I’m one of those people that
take apart a clock to figure
out how it tells time. (P3)
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Table 5
Final Codes, Categories, Themes, and Examples for Research Question 2
Final code Category Theme Example quotation
Digital
Preparation
Confirmation of
technology
perception
Self-assessment of digital
literacy
I am always looking for ways to
improve on what I need to know.
There is always room for
improvement, and I know that
especially with technology. P6
Support/Resources
Learner
comfortable
with and
objective about
the use of
technology.
Support to
successfully
perform in
digital
environments
Needing organizational support I know I am doing the best I can.
Sometimes I feel like teachers
forget they were once a student and
do not put themselves in our shoes.
Things are so different these days. I
never thought that submitting an
assignment would be a barrier for
me. P4
Social Factors
Essential
technology,
technology
advocate, 24/7
support
Enhanced digital learning
workflow
My kids help me all the time with
this, without their help I would not
know what I was doing with any of
this. I am so glad they know how to
do this, and they are encouraging
me not to be afraid of using it. I feel
like I am going to break something
all the time. But it makes me feel
better to know that my kids are my
resource to get me through. P2
Comprehension Intellectual
capacity,
conceptions,
knowledge,
grasp,
understanding
Benefits of technology learning
outcomes
I understand things different and so
I say that is because how I talk, and
they talk. I talk, they talk is different
to understand maybe, so it’s
confusing So, uh, it’s hard at times,
uh, but I am working alone and
doing the best I can. P11
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Results
The purpose of this basic qualitative study was to explore the learning supports
and types of learning required for digital literacy development of undergraduate college
students over 50 attending four-year institutions. Themes were developed from the
precodes and emergent codes. Four themes and one sub-theme resulted from the analysis
of the data. For the first research question, I identified the themes: self-assessment of
digital literacy and technology learning outcome with the sub-theme buy-in of LMS. For
the second research question, I identified the themes: organizational support needs for
skill development and enhanced digital learning workflow. The results are organized by
research questions and the themes associate with them.
Research Question 1
The first research question asked how college students over 50 years of age
describe their learning as they develop digital literacy skills. Two themes emerged from
data analysis: self-assessment of digital literacy and benefits of technology learning
outcomes.
Theme 1: Self-Assessment of Digital Literacy
Beyond the understanding required for basic access to various technologies,
becoming digitally literate requires a variety of abilities. Adults with limited familiarity
with digital environments can improve their understanding of technology through
practice and guided training, according to research. The first theme describes how each
participant self-assessed their level of digital literacy and how they determined their
rating. Participants in this study reported similar assessments about their technological
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ability, with 10 out of 12 giving themselves a rating of lower than my peers. Participants
expressed a commitment to actively develop and improve on the necessary skills to gain
digital competence to perform and access the digital platform required to complete
courses in their LMS work and improve technology learning outcomes. Participant 4 was
a campus-based junior student who talked about her commitment to do whatever was
necessary to be successful. She said:
When I decided to go back to school it was a commitment that my family and I
decided to make together. Of course, technology is something that does not come
easy to me and it’s not second nature to me at all, but I am giving it all I have
because I have no choice really. Since every class I have taken has required
assignment submissions, discussion posts, and all communication done the same
way, it has allowed me to get used to using it and getting somewhat of a
comfortability with using the system.
Participants talked about the use of technology being a type of communication
barrier and not feeling comfortable using technology at all. Ten out of the 12 participants
gave themselves a digital self-rating of lower than their peers. Participant 2, a junior
attending a campus-based institution, talked about school never had been difficult for her
in the past. She stated, “I never thought not using paper and pencil to submit assignments
would cause me as much anxiety as it has with using technology.” Participant 9, an
online senior, talked about using technology as a way of life. She said:
Everything we do now involves technology in some way shape or form. I cannot
think of anything that we do in life that does have something to with using
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technology to assist us with everyday living. Going back to school to get my
degree is something I thought about doing for a long time, but I did not think
about if I went back that technology would overwhelm me this much. It is not the
curriculum but how I have to show that I know what I am learning by using the
leaning system they make us use.
When using the LMS, there is a level of flexibility and acceptance that must be
achieved, as participants acknowledged. Participant 9 stated that the aim of educational
success will not be reached without acceptance. Participant 3, a campus-based junior,
shared that he had been employed at his current job for almost 41 years. He talked about
how going back to school was a necessity because he recognized the younger employees
operated technology faster and more efficiently. He said:
As a supervisor, it is my responsibility to train my employees. I cannot fathom not
being able to use the technology required to do our job let alone not being able to
confidently use the technology I am required to use to complete my assignments
for class. I feel like a duck out of water so to speak. I have been really outside of
my comfort zone with school primarily because of the technology we have to use.
At work it is getting better because the system we use is repetitive. At school, its
repetitive but when something goes wrong with it or shuts down or a download
does not happen, I am lost.
This
theme demonstrated that participants self-assessed digital literacy was often a
hurdle for students from the start of their return to college, and for some in the workplace.
They wanted to expand their skills by understanding and using learn
the LMS because
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technology was critical to their educational success. Their comfortability of the process
was based on the support they identified and their willingness to adapt and learn.
This sub-theme refers to a series of comments from the focus session
participants who said that they did not have to like the LMS, yet they recognized they had
to adapt to using it, describing this process as “buy-in.” According to all 12 participants
in interviews and in focus groups, learning in a digital context while using the HEI’s
mandated LMS to complete school tasks was overwhelming and challenging. Participants
also stated that either insufficient or no training was provided to prepare them to use the
LMS. Due to the lack of direction, participants claimed that it appeared that it was
expected they already understood how the technology worked. FG
All participants reported a lack of understanding about how to use the LMS for
learning. Participant 9 stated that she had never heard of an LMS before, or that it would
be necessary for her to communicate with others throughout her coursework. Throughout
the interviews and focus groups, this declaration proved to be an anthem. For example,
Participant 11 stated, “I would have researched more into the university to see how
learning took place and then made sure I had all of the necessary equipment that I needed
to be successful.” She went on to say, “once I would have learned what I needed to be
successful, I would have made sure I knew how to use D2L so that I could be on course
with other students.”
Some participants saw their lack of preparation to use technology as a reason to
consider dropping out. Participant 7 from Focus Group 3 reported that she was on the
verge of giving up because she could not figure out how to navigate the LMS. She stated:
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When I went to my very first class, I was so excited and by the end of it almost in
tears because I was so overwhelmed not with the course work itself but with the
system required to turn everything in…it was so foreign to me.
Participant 3 reported that his institution did not provide adequate preparation and only
gave students a piece of paper with seven steps to follow to navigate his LMS. He said:
I am a fairly fast learner I’d like to think, yes, I learn by doing but I do not think
handing me a piece of paper and telling me to have at it is conducive to me
achieving great success. I mean I have invested a lot of time of money into my
education this time around and I do not want technology of anything to get in the
way of this.
Similarly, Participant 5, from Focus Group 2 shared even as junior she still felt
overwhelmed. As did others she often felt unprepared. “Sometimes I feel that technology
was put in place to weed out or screen who can cut it and who cannot.” Even though
participants felt unprepared using technology they accepted the fact that it was necessary
for the success of their learning outcomes.
Theme 2: Benefits of Technology Learning Outcomes
The second theme also answered the first research question. The benefits of the
technology learning outcomes theme describes how technology supported student
learning which provides insight into the learning environment while learning was being
facilitated. A technology learning outcome is essential and significant learning that
students achieve by using technology and relates to their digital literacy. Students
benefited from technological learning outcomes because they achieved faster and more
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efficiently and the LMS provided more engaging opportunities to practice what they had
learned. For example, Participant 9 from Focus Group 3 reported, “When I decided to go
back to school, I was worried about what it would be like because it had been so long
since I had been in school.” Technology is one of the things that students in my age group
should be aware of, as it is not going away and is only becoming more cemented in
education. Interview participants 6 and 7 also expressed concern about having the
intellectual capacity to comprehend technology and use digital resources to attain their
educational goal. Participant 6 from Focus Group 2 said, “I understand that I learn by
doing things over and over so the more I use the system the more I will grasp how to use
it effectively and I will also get more comfortable with it.”
Students were also concerned about having the capacity to work independently in
an online environment. Participant 7 discussed how learning online was convenient but
the barrier for her was not having physical contact with someone. She said,
since the beginning of my program, I have been in the classroom and able to ask
my instructors questions right after class or schedule an appointment to go to their
office hours. Now, since COVID, there is a gap in waiting and the question I have
I sometimes do not remember or remember the right way to ask my instructor
since the question is not fresh on my mind.
Students also recognized their need to improve their digital literacy and actively
acquire it on their own. Interview participants 5 and 9, and Participant 7 from Focus
Group 3, stated that they needed any form of resource to manage the LMS at their HEIs,
including tutorials, manuals, workshops, 24/7 support techs, faculty support, and
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institutional help. They discussed how the LMS was the primary source of information
that contributed to their learning success. They agreed that they could not learn unless
they were digitally literate and could use the tools required to complete coursework.
Research Question 2
The second research question asked what types of support do college students
over 50 years of age report most helpful in their development of digital literacy? The data
generated themes identifying two preferred forms of support: organizational support
needs for skill development and enhanced digital learning workflow. Both interview and
focus group participants statements showed a consensus through their statements of their
experiences.
Theme 3: Needing Organizational Support
The theme organizational support needs for skill development refers to what all
participants said they desired to feel and see from their HEI in terms of their learning
progress, technology knowledge, and help needs. all of the participants stated they
preferred to acquire digital literacy by repetitively practicing so that they could improve
digital skills while receiving an assessment with feedback about how to improve.
Participants’ feedback described growth of digital literacy in regard to types of support
they felt they needed at the start of their college experience to ease them into using the
required technological platform for courses.
Students described their ability to succeed in a learning setting by utilizing the
technology platform to acquire digital literacy with the support of their HEI. Participants
frequently used the words assistance, advice, preparation, training, and practice to explain
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how the LMS supported their work. These terms functioned as important indicators of
why support is necessary in the development of digital literacy abilities. All 12 interview
participants discussed their unfamiliarity with an LMS which was required to complete
coursework. For example, Participant 1, a campus-based junior, reported that using a
program to submit assignment was different but seemed practical. She stated, “I want to
always work smarter and not harder, so being able to submit assignments and just
communicate using the computer is best and besides my handwriting has gotten worse
over the years.” Participant 8 discussed the need for updating professional knowledge
through training needed for work and academic success. She said:
Everything seems so rushed these days. What happened to training to get prepared
to do your best. I use YouTube videos sometimes to show me how to submit
assignments and I have to pause and replay it a few times just to catch up with
what is being said.
Students did feel they needed some preparation for relying on technology for
learning. Participant 11, from Focus Group 2 a campus-based junior, said training was
necessary for technology use because it is not second nature to most people.
A computer may seem like a foreign object to people in my age group. I use a cell
phone, I have a computer and even an iPad that I have been using for a few years
now, but to go from the general use of a pencil and paper, to completely using
technology, no wonder no one can write anymore. I have always felt that I had a
confidence in myself about most things, but that learning management system
shook me because I have to use it to be successful and there is no way around it.
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Participant 6 said that,
Although technology is not new to me, using it in this way to look for
information is. Everything we submit and every way we communicate is through
this system. I am getting used to it, but it is a whole new way of life for me, it
really is.
Participant 2 reported during Focus Group 1 that she was almost at the end of her
program, but she still was not comfortable with the learning platform. She said,
I have a year and some change left, so you would think I would be a pro at using
the using the system. Not a chance. Technology is not something that I have ever
gravitated toward. I am not one of those people that think technology makes life
easier. It has made my life difficult for the past 4 years in school.
This theme highlights that the most important factor for students is assistance in
developing their digital literacy skills. By continuing to use the LMS necessary at their
educational institution, they increased their digital literacy abilities which increased their
future work skills.
Theme 4: Enhanced Digital Learning Workflow
The fourth theme also aligned with the second research question. Participants
specified what kinds of support they found most beneficial in their digital literacy
development. For them, the most effective support enhanced digital learning workflow.
This theme refers to how participants figured out how to use the HEI’s methods or
actions needed to operate the technological learning platform. The definition of enhanced
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digital learning workflow is the plan or system that details the processes or actions that
are provided by the HEI as resources to operate the technology learning platform.
When expressing the types of help needed to increase digital technology abilities,
participants used terminology like workflow, organization, structure, or roadmap.
Participants stated that instructors encouraged them to seek out any form of resource that
would assist them in successfully using the LMS anytime when they had trouble with it.
Some instructors included instructional steps on how to access various portions of the
LMS in their syllabus and during course teaching, according to participants, and this
made a difference. Participants reported that having access to more support 24 hours a
day, 7 days a week would be beneficial because their learning or knowledge of a
procedure may differ, help may be needed day or night, and that one-time instruction was
insufficient.
Participants had a lot of concern over operating the LMS. They expressed a desire
for structure in order to develop a solid understanding and competency in order to
successfully manage the system. Participant 5 from focus group 2, an online junior,
reported that she needed structure especially when using technology. She stated, the
“course curriculum is structured so the learning platform that students are required to use
is functional and comprehensible.” Participant 3 from focus group 1 stated that “specific
instruction is warranted so that there is guidance for us to know what we are doing.” She
explained that her grandchildren” are my little roadmaps helping me navigate through
school.” Participants 4, 6, and 8 also stated how family members also helped with
navigating the LMS. Thus, organizational structure was more than the course materials
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but also how to navigate the system when submitting assignments, communicating via
discussion posts, completing exams, and or with questions in general.
LMS course structure and directions helped Participant 4 better comprehend how
this technology facilitated the learning process. Another technique for developing a
functional workflow is personal organization an additional strategy for building structure.
Participant 3 from focus group 1, a campus-based, junior stated that his personal
organization is what helped him operate his HEI’s LMS. He stated:
I have to be organized in order to make heads or tails of what I am doing. I have
never operated anything like this, Desire2Learn is very new to me, but at the same
time I see why it is beneficial to students and teachers. This way it seems to keep
everyone honest because everything is submitted and completed in this system.
Participant 4 was unique in that she used her occupational workflow to better
comprehend the educational workflow’s constructs. A campus-based junior, she talked
about working at one place for nearly 30 years and that workflow was an important
component of any learning setting. She said:
Canvas is the system that we have to use to submit all of the assignments and also
what we use to communicate especially now because of the pandemic. I brought
up my job and workflow because when I first started back to school no one asked
if I was familiar with the system, it was just part of the curriculum and required so
everyone has to use it.
Participant 5 from focus group 2 stated that it is the responsibility of an HEI to
make clear how to use the LMS. She also reported that younger students used the LMS
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system with ease because they were used to it. She said, “in my daily life, the only time I
use technology is on my cell phone, my computer when I have to send an email or put
information on a spreadsheet, and I guess the television at home.” Thus, the younger
students had integrated the workflow into their study habits while she, and perhaps her
peers, had not.
Situations that were discrepant or ambiguous were discussed during the focus
group sessions, then I looked over field notes and transcriptions for clarification and
inclusion in the analysis. The only discrepancy in the results was Participant 1 who had
prior knowledge and extensive professional experience with technology prior to returning
to school. She rated herself with skills higher than her peers. While her skills may have
been an asset as she completed coursework, she did have to learn new systems which
were unfamiliar. In this way, she was similar to her peers but different in her level of
general digital literacy which was above her peers. The research questions did not focus
on the distinction of different levels of digital literacy, yet it may be that prior use and
practice of online tools are advantageous when learning to use an LMS.
Summary
This study included 12 undergraduate college students over the age of 50 from six
different 4-year colleges across the United States. Initial individual interviews were
conducted over a 3-week period, from which two focus group sessions were held. I noted
patterns, areas of consensus, anything that was unusual, and issues that needed
elaboration or clarification after reviewing the interview data to establish a foundation for
the two focus group meetings (Barbour, 2007; Merriam & Tisdell, 2016). Six participants
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took part in one of two Zoom focus group sessions. Each focus group session consisted of
three participants who were asked questions based on the interview data analysis.
I looked over notes and transcriptions for clarity and inclusion in the analysis after
identifying any discrepancies or ambiguities. The process of data collection, reporting,
and data analysis required an iterative review during the interview and focus group
session process, which was not interacted. I took notes on the participants’ statements
that directly addressed the question, sparked my curiosity, or that I wanted to confirm. I
utilized NVivo software to further organize and analyze after coding and conducting
preliminary analysis.
Data analysis involved several steps. I started by creating precodes based on the
fundamental constructs of my conceptual framework models. After analyzing individual
interview transcriptions using the precodes and adding new codes, I reviewed previous
analyses to ensure I did not overlook a pattern that may have emerged throughout the
data set. I began with the interview data and built a foundation for the focus group
sessions by analyzing it. For both data sets, I then identified and applied emergent codes
that were supported by particular remarks from interviews and focus group sessions.
Then I created categories to serve as the foundation for themes. Themes were then
constructed based on the keywords and phrases provided by the participants.
The results were articulated in the themes: self-assessment of digital literacy,
benefits of technology learning outcomes, organizational support needs for skill
development, and enhanced digital learning workflow. Student needed to learn while
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developing technological skills resulting in digital literacy. Family, peers, professors,
social media, and HEI support were all sources of assistance.
The key reason over 50 learners reported needing support was their inexperience
with using technology for learning, particularly the LMS. They noted that technology was
not required daily in their lives unless it was for school. Moreover, adjusting to using the
LMS as a requirement for learning was difficult, particularly when support was not
readily available.
When LMS support was introduced into instruction, students took ownership of
their learning through an enhanced digital learning workflow, with organizational support
needs for skill development, aiding in a self-assessment of digital literacy. Students were
already driven to seek out alternative resources to facilitate their digital literacy because
of the assistance provided by the organization. Participants said they desired a learning
environment that was assisted and guided through collaborative and interactive learning
experiences because they were not used to using technology, but that was not provided by
their HEI’s. Their digital literacy development was supported by resources including HEI
services, their children, YouTube, and peers.
The focus groups confirmed that there was a need for increased assistance from
their HEI with digital literacy. Participants discussed a number of positive aspects of the
LMS that was required. They agreed that because technology was required, there was a
need for training, support, and confirmation that all students could successfully operate
because HEIs assumed that all students understand this type of learning platform.
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Chapter 5 offers an interpretation of the findings in reference to peer-reviewed
literature and conceptual frameworks, a description of the study’s limitations,
recommendations for future research, implications for social change, and a final
concluding statement.
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Chapter 5: Discussion, Conclusions, and Recommendations
The problem addressed in this basic qualitative study was that the over-50
population of students lacks the digital skills or habits of use with technologies required
of them while in college, thereby creating a disadvantage in their social, educational, and
career opportunities as college students (see Jacobson et al., 2017). The purpose of the
study was to learn what supports and types of learning are required for undergraduate
college students over 50 attending 4-year institutions to attain digital literacy. The two
conceptual frameworks used to guide this study were digital literacy (Tsai et al., 2017)
and Mezirow’s (2009) transformative learning model. I used each framework to interpret
how students over the age of 50 acquired digital literacy and what best supported their
skill acquisition.
The college students over 50 years of age who participated in this study required a
variety of technical supports, some of which were provided by their HEI while others
were self-obtained. Participants said they received help that was informal (e.g., from
family or social media platforms) and formal (via instructors, peers, or HEI-provided
tutorials). However, assistance was not always available when needed, posing a dilemma
for students unfamiliar with their HEI’s LMS or other required technologies. Standing
out from their peers, two of the participants claimed to be self-sufficient in their use of
digital technology by giving themselves a digital technology self-rating of higher than my
peers, and neither reported requiring supports from the HEI or other resources.
Participants stated that they benefited from technology support either because
they did not have prior knowledge of technology in general or they had no experience
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with their HEI’s required LMS. The findings revealed that with some training, learners
over 50 can use technology and enhance their digital literacy skills with reasonable ease.
However, digital technology support networks must be in place and not assumed to exist
by HEIs.
Findings indicated a gap between the needs of digital natives and the digital
immigrants who required assistance. Participants felt that academic achievement,
technological support, and instructional methods impacted their collegiate and life
experiences in the process of acquiring digital literacy. This chapter includes
interpretation of the findings, study limitations, recommendations, implications, and
conclusions.
Interpretation of the Findings
The attitudes and perceptions of the 12 interviewees and focus group members
confirmed the findings of previous research reviewed in Chapter 2. In this section, I
provide an interpretation of the findings in the context of the conceptual framework and
peer-reviewed literature. The immediacy of using technology for learning appeared to
require different social interactions (see Gilster 1997; Ray 2018) as a form of Mezirow’s
(2009) communicative and instrumental (problem solving) learning.
Interpretation of Findings Through the Conceptual Framework
The findings were consistent with the conceptual frameworks for this study:
digital literacy (see Glister, 1997) and Mezirow’s (2009) transformative learning model.
For this study, I focused on two of digital literacy’s four principles: interdependence and
social factors (see Glister, 1997; Osterman, 2012; Ray, 2018). The two principles that
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were demonstrated in this study were social and interdependence transformational
learning tenets that students felt were related to academic achievement. Findings
indicated that the social context present during coursework influenced students’ academic
progress.
Interdependence is a mutually responsible relationship between people who share
a standard set of beliefs (Kara et al., 2019). This relationship can provide a collaborative
learning environment in which learners work together to achieve common academic
goals (Glister 1997). Transformative learning involves two kinds of learning: (a)
instrumental learning, which focuses on learning through task-oriented problem solving,
and (b) communicative learning, which involves how individuals communicate their
feelings and desires (Howie & Bagnall, 2013; Mezirow, 2009). Current participants
explained they believed their peers had used an LMS since K–12 and that their HEI
seemed to assume that all students had a background using this type of system. This
confirms Glister’s (1997) tenet of interdependence. These results validate Mezirow’s
(1997) transformative learning theory in how over-50 college students faced, possibly for
the first time, a technological universe with which they were unacquainted, generating
what Mezirow (1997) referred to as a change in an individual’s frame of reference,
thereby triggering new learning to occur.
In addition, participants stated in the focus groups and interviews that they
reached out to their family members and instructors for navigation guidance when using
the required LMS, which reflected Mezirow’s (1997) concept of communicative learning.
Participants reported that their family members, notably their children or grandchildren,
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provided most of their technological support, reflecting Gilster’s (1997) factor of social
interaction as necessary and a component of digital literacy. Social factors indicate why
an individual may need specific support and which social conditions contribute to
improved digital literacy (Gilster, 1997; Ray, 2018). Tsai et al. (2017) determined that
support from primary social groups provides a form of environmental support, which was
confirmed in the current study. All participants reported receiving support from family or
friends as they maneuvered through their LMS. Additionally, societal factors may
influence a person’s acceptance of or use of digital tools, as well as encouragement to use
the internet. These factors are especially important for older students who may not be
digital natives and may require a lot of technical and social assistance (Meyers et al.,
2013).
Interpretation of Findings Related to the Research Questions
In regard to RQ1, participants described their technology skills through their
digital self-rating as generally lower than their peers. Most participants reported that they
did not have to use technology in their prior educational experience. They said the
requirement to use technology as a primary tool for their educational success was
overwhelming. They were not digitally literate, as described by Glister (1997) who
indicated that a digitally literate person possesses a set of skills that include knowledge
acquisition, information analysis, internet access, and hypertext navigation. Although
student participants were not completely digitally illiterate, they required assistance in
learning how to use the LMS so that they could learn. The immediacy of using
technology for learning appeared to require different social interactions (see Gilster 1997;
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Ray 2018) as a form of Mezirow’s (2009) communicative and instrumental (problem
solving) learning.
Over-50 participants in my study reported that they can understand how to use
this technology if the HEI provides materials to students so that they can perform the
required activities. Interdependence in education, particularly for college students over
50, establishes a collaborative learning environment in which individuals collaborate
toward shared academic goals (Kara et al., 2019; Perez, 2018; Rashidian et al., 2018).
This form of learning fosters a sense of self-awareness about how students perceive and
comprehend their experiences in their social environment (Kara et al., 2019), as current
participants reported in their reliance on others, especially for just-in-time learning.
Findings related to RQ2 provided insight into the processes used by over-50
students and the supports that aided them. Participants reported that they felt
overwhelmed at the start of their academic degree programs because technology was an
essential part of how they had to communicate, submit assignments, complete exams, and
achieve overall academic success. However, participants also stated that with consistent
use of the HEI’s LMS, their digital skills improved. The notion of an LMS was so
unfamiliar that participants had to adopt a new way of thinking about how to learn and
reframe what it meant to use technology for learning. Participants employed instrumental
learning when they learned how to use the LMS required by their HEI to succeed in their
courses. Additionally, they received assistance from both informal sources (e.g., family
members) and formal sources (e.g., computer orientation) but reported that support was
often not available when needed. Vaportzis et al. (2017) found that most older learners
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are ready to acquire new technology and are willing to learn how to use it. However,
current participants expressed concern over a lack of clarity in instructions and help.
Findings revealed that a social support network can be a just-in-time support for older
adults who have gaps in their digital literacy, which confirmed Ray’s (2018) notion that
social factors can support digital literacy. To some extent, current findings confirmed
those of Jin et al. (2019) who found that family support, friend support, and personal
characteristics such as technology confidence could be a necessary resource for some
older students.
Current findings also suggested that self-efficacy may play a role in the
acquisition of digital literacy, as illustrated in the participants’ desire and willingness to
learn new technology. Other studies indicated that older adults are receptive to learning
and using a new technology in certain circumstances, for example when they are curious
about changes in society or have a desire to be digitally conversant though the use of
multiple new technologies (Costa et al., 2019; Vaportzis et al., 2019). Being motivated to
achieve a personal or professional goal, combined with a social support system, may be
beneficial to older college students in their acquisition of digitally literacy. Tsai et al.’s
(2017) findings revealed that older students are confident with routine uses of
technology, such as phones and internet access, but they may not understand digital
concepts related to skills required for a college degree, such as relying on the LMS to
learn. For older adults, a significant obstacle to discovering a variety of modes of
technology has been an absence of their personal digital literacy (Jacobson et al., 2017).
Current findings added to those from prior research.
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The need for social support is not new (Liu et al., 2020); however, having just-in-
time support is unique. Whether through interactions with more knowledgeable
acquaintances or through the use of tools designed to offer technology learning through
information and feedback, over-50 students require support in the moment they are
confronted with a lack of knowledge. Current findings not only illustrated digital literacy
and transformational learning but also offered evidence of a need for HEIs to offer these
types of experiences and supports for older students to help them become agile in
learning through and with technology.
Limitations of the Study
There were three limitations in this research. The study included 12 people
participating in interviews, followed by two focus groups with three people each. As a
result of the small number of participants, the results are limited in their generalizability.
This study provided a group of students with an opportunity to discuss their feelings of
being unseen. Participants volunteered to take part in the study, which could indicate that
they had a unique perspective or trait that I did not look for. Although there was a wide
age range (50–60), it is likely that self-selection reflects a tendency or preference for
technology (Leedahl, 2020).
The COVID-19 pandemic restrictions were the second limitation. It is possible
that some data were missed because I could not interview participants in person or hold
focus groups in person. For example, I was unable to observe facial expressions, body
language, and other nonverbal cues. Furthermore, older students may not have felt as
comfortable communicating for long periods of time using technology as they would
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have face-to-face. In a perfect world, interviews and focus groups would have been held
in person. Although I was able to obtain all of the data, scheduling issues and reliance on
technology (in a group that was already technologically challenged) may have limited
what participants reported. A final limitation was omitting demographic data. There may
be subtle differences between online versus campus-based students, preferences by sex,
and influences of prior education or work.
Recommendations
Given the increasing population of older college students and the increase in
online coursework, digital literacy is key for the older student’s academic success.
Further research is warranted in several areas. The first recommendation is to identify
HEI strategies that create support networks in which help is readily available on a
constant basis to students who need assistance with LMS navigation. Current findings
demonstrated that offering ongoing support can help students deal with technology-
related issues as a form of just-in-time learning. The outcomes of this study and the
alignment with communicative and instrumental learning (see Mezirow, 2009) facilitated
through social and interdependent interactions (see Glister, 1997) affirmed that there is a
need for future research to examine how institutions are serving adult learners because of
their increasing population, generational differences, and institutional structures that will
change over time.
A second recommendation is to replicate this study with a larger sample size
using a quantitative survey based on the contrasts between student personal technology
use and the technology required for learning. For example, the International Assessment
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of Adult Competencies program has been used to determine the digital competence of
older adults. A larger sample using a quantitative measure would provide detailed results
and address more diverse populations with demographic information not factored into
this study (such as gender, subject area, etc.). Future research may reveal subtle
differences but using a larger sample size in a quantitative analysis may allow researchers
to use data to show a significant relationship between the over 50 population successfully
using technology for personal use and failing to successfully operate an LMS in an HEI
for educational use.
A third recommendation is to examine stakeholders’ perspectives and practices
regarding over-50 college students, including advisors, student services, information
technology support, and instructors. Stakeholders must keep up with shifting
technological trends in the HEI learning environment (Choudhury & Pattnaik 2020). The
issue that stakeholders face is rapid technical improvements and corresponding changes
in the learning environment, which, when handled effectively, results in an effective
digital learning environment. Another suggestion for future research is to include
stakeholders as participants to seek their advice and understanding on better supporting
over 50 college students in their academic endeavors. Interviewees might include
instructional designers, technology trainers, administrators, and faculty.
A fourth recommendation is to look at over-50 college students and their
confidence level with digital literacy. Self-motivation (Sentino, 2021) and self-efficacy
(Aldhahi et al., 2022; Sumuer, 2018) were suggested by findings but not specifically
addressed. Given the shift to emergency remote teaching (Xie & Rice, 2021) because of
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the COVID-19 pandemic it may be that learning independently because there was no
other choice could reveal personal characteristics of those who were capable of
successful technology use. Additionally, comparing digitally proficient and non-tech
savvy population of over-50 college students might reveal their psychosocial traits that
indicate their preparedness to use technology in their coursework. Moreover, it is unclear
if digital competence in the workplace or in one’s personal life can be transferred
effectively to an academic setting.
Implications
The results of this study have implications for improving the well-being and
success of older college students as well as for the approach used to study this population,
and how HEIs may consider supports and services to this population. In this section I
discuss the implications for practice, research methods, and ideas for positive social
change.
Positive Social Change
The findings of this study can affect positive social change when HEIs recognize
the unique needs and attributes of the over-50 college student. Students in this study
stated that they often felt invisible because their HEI assumed that all students could use
the mandatory LMS, and other tools, to achieve academic success. Because the LMS is a
critical component for academic success, over-50 college students’ dignity and worth will
be fostered once they believe that their HEI promotes their growth of digital abilities. The
unspoken bias toward and possible exclusion of older students assumed digital
competence not only puts them at a disadvantage but contributes to a generational divide
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of equity. When HEIs offer services and acknowledge the unique needs of this population
they will also bring an awareness of the value of technological support and the necessity
for continued training of digital literacy skills of students that require it potentially
equalizing opportunity for all generations.
Lastly, the COVID-19 pandemic changed the educational environment
dramatically throughout this research period; traditional in-person learning migrated
online learning through emergency remote teaching (Xie & Rice, 2021). Thus, a
recommendation is to replicate the study in a post-pandemic period to determine if older
students who continued learning during the pandemic acquired digitally literacy, had
access to different supports, or were able to succeed in different ways when confronted
with forced changes in how to learn. It may be that the pandemic inadvertently equalized
the digitally literacy gap for digital natives and immigrants. Participants thought that
academic achievement, technical support, and teaching approaches influenced their
collegiate and life experiences in developing digital literacy. Participants believed that to
be effective in the digital classroom, they had to learn recent technologies and develop
technological abilities they their younger peers had already acquired.
Methodological Implications
Rather than a qualitative design, correlational or causal comparative/quasi-
experimental could reveal relationships between level of competency and other factors,
such as discipline of study, age, sex, or workforce history. An assessment would be given
to each student from different programs of study to assess their digital literacy skills at
the beginning of their entry into the HEI (Bin Mubayrik, 2020). This assessment could
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identify a need for or type of training to improve digital literacy. In addition, an
examination of the evidence and an evaluation of the advantages and disadvantages from
the assessment of the over-50 population not receiving support or training would provide
insight for developing digital literacy (Merriam & Tisdale, 2015). Using this
comprehensive measure and observation against some objectives and standards or
comparing and contrasting will evaluate the learner’s technological progress (Yambi,
2018). Looking at learners in different programs of study and different institutions would
gather demographic information and assess perspectives, barriers, and attitudes of the
over-50s toward the adoption of other technologies (Wang, et. al. 2019).
Recommendations for Practice
HEI’s have an opportunity to determine whether or not students who enter college
are ready to use the technology required of them. Once a student is admitted into an HEI
an assessment could be implement using the HEI’s LMS to gauge their competency. In
this way the student would be introduced to the system while being assessed of their
digital competency.
Once the assessment is completed the student would be required to enroll into a
training seminar to ensure their understanding of navigating the LMS system. The course
could be counted as a one-credit hour free elective completed during the first semester of
their academic career. At the end the of the semester, the assessment could be given again
to gauge the students’ new understanding of the LMS. HEIs assessment of older adult
learners’ entry-level skills as they enter college.
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Such an assessment given to every student over 50 years of age can gauge what
type of technology abilities students have, and the kind of training students need to
navigate the institutional LMS to be successful. Additionally, this type of assessment can
empower and identify those specific areas in which the student needs to learn, targeting
an identified deficit. A student may only need to know a particular process, tool, or
strategy.
Using the assessment for a personalized training program can empower the
learner and customize their academic experience. This assurance would promote this
population’s worth, dignity, and development. HEIs have an opportunity to shift away
from assumptions about student’s digital literacy to document their entry-level skills and
abilities.
In addition, college students learning digital literacy skills to function
academically at 4-year institutions require guaranteed support of their HEI. An
assortment of supports and learning experiences that include hands-on practice can
encourage self-reliance and practice before entering a course. This assessment will
support this population of over-50 college students as they develop digital literacy skills
they will need to succeed and participate in their future careers.
Conclusion
This study provides a better understanding of the over 50 college students’
experience developing technology skills. Returning to school for a professional degree
can be intimidating if a student has never used or is not familiar with the technology
required for academic achievement. According to all the study participants, receiving
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continuing and just-in-need support is difficult, especially when institutions assume older
students have learned or will learn the LMS on their own and intuitively.
The assumption that all students enrolling in a 4-year institution will grasp the use
of an HEI’s essential technology requirements is an error. An effective system for
students to gauge their digital literacy is a critical strategy for HEIs. Higher education
should provide effective and sufficient support to students using mandatory technology in
their college curriculum. Moreover, building support around a digital literacy framework
and not only the LMS will expand the older student’s digital literacy beyond college. To
not do so risks the success of students and may cause institutions to fail in their mission
of degree completion. The findings of this study may assist higher education institutions
in providing better support for these learners, thus balancing opportunities for students of
all ages.
