Examining Digital Literacy Competences and Learning Habits of Open and Distance Learners, Contemporary Educational Technology

Abstract

he purpose of the study is to examine digital literacy competences and learning habits of learners enrolled in the open and distance education system of Anadolu University in Turkey. Data were gathered from 20.172 open and distance learners through a survey which included four parts: demographic information, abilities to use digital technologies, learning habits, preferences in using digital technologies for learning purposes. Principal Component Factor Analysis was applied in order to group and classify the attitudes and statements of the learners in their personal learning preferences, problem solving skills, project work skills, and abilities to use digital tools for learning purposes. Their personal learning preferences produced five factors: visual, auditory, dependent, collaborative, and reading- writing learning styles. According to the results of the study, learners believe that they have problem solving and project working skills to deal with educational difficulties. However, they seem to have only basic competences of digital literacy and the skills to use information and communication technologies at a basic level. They need training on how to use digital tools more efficiently for learning purposes. Further research is needed to explore how to increase the use of digital tools for the purpose of effective learning and also how to design learning environments to improve digital literacy of open and distance learners.

Full text

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Examining Digital Literacy Competences and Learning Habits of
Open and Distance Learners
Nilgun Ozdamar-Keskin, Fatma Zeynep Ozata, & Kerim Banar
Anadolu University, Turkey
Karl Royle
University of Wolverhampton, United Kingdom
Abstract
The purpose of the study is to examine digital literacy competences and learning habits of
learners enrolled in the open and distance education system of Anadolu University in
Turkey. Data were gathered from 20.172 open and distance learners through a survey which
included four parts: demographic information, abilities to use digital technologies, learning
habits, preferences in using digital technologies for learning purposes. Principal Component
Factor Analysis was applied in order to group and classify the attitudes and statements of
the learners in their personal learning preferences, problem solving skills, project work
skills, and abilities to use digital tools for learning purposes. Their personal learning
preferences produced five factors: visual, auditory, dependent, collaborative, and reading-
writing learning styles. According to the results of the study, learners believe that they have
problem solving and project working skills to deal with educational difficulties. However,
they seem to have only basic competences of digital literacy and the skills to use information
and communication technologies at a basic level. They need training on how to use digital
tools more efficiently for learning purposes. Further research is needed to explore how to
increase the use of digital tools for the purpose of effective learning and also how to design
learning environments to improve digital literacy of open and distance learners.
Keywords: Open and distance learning; Digital literacy; Learning preferences; Learning
styles; Problem solving; Project skills
Introduction
Mega-universities are distance teaching institutions with over 100.000 active students in
degree-level courses (Daniel, 1996) and intend to meet the adults’ and lifelong learners’
educational needs. Some mega-universities especially in developing countries such as India, Iran
and Turkey have the largest enrollment of active students (over 1 million) across all campuses
(including off-campus). Anadolu University, as one of the mega universities, has laid the
foundations of distance learning in Turkey and it consolidates its position with open learning and
teaching activities carried out over the last 30 years and today. It serves over 1.5 million open
and distance learners with open teaching institutions including three faculties: Faculty of Open
Education, Faculty of Business, and Faculty of Economics. Anadolu University provides a variety
of delivery options such as visual classrooms, e-learning portals, e-portfolio systems, and
interactive books where individual and cooperative work is supported and learners are

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encouraged to use technology in their learning. These groups of learners are quite diverse in
terms of age, digital literacy competencies, and learning habits.
This study is a part of “A Multiplatform M-Learning System for More Qualified Courses in ICT
Era” project sponsored by the British Council and Anadolu University. The focus for the project
is the development, deployment, and evaluation of a mobile application to support
undergraduate/graduate programs (Ozdamar-Keskin et al., 2012). Anadolu University, Okan
University, and the University of Wolverhampton have worked together on surveying the digital
'habits', which is access, expectations, and experiences of mobile digital technologies of their
staff and student populations. This study examines the abilities of Anadolu University’s open
and distance learners to use digital technologies in digital life and to use these technologies for
the purpose of effective learning; it also identifies the general profile of learners by analyzing
their digital literacy and learning habits for designing mobile qualified courses. The findings
gathered in the study may be used to assess the learner needs regarding digital literacy. Thus,
administrators, education developers, researchers working on innovative or creative learning
projects may benefit from understanding the learner profiles in these areas by making a greater
match or fit between learners and the programs.
Digital Life, Digital Literacy, and Learning Habits
With the opportunities provided by information and communication technologies, the most
important value of the 21st century is 24/7 access to information in the fastest way within the
frame of one needs. With the advent of mobility, digital tools have become an inseparable part
of people’s lives and also enhanced three meaningful factors such as speed, virtuality, and
networking (Rivoltella, 2008). According to a recent report published by the International
Telecommunications Union (2013), there are 7 billion mobile customers throughout the world;
27% of these users benefit from 3G/4G mobile services. The latest numbers which were
announced in 2013 by Apple show that 50 billion applications have been downloaded in the last
5 years. Many people access to information they need via web sites such as Google, YouTube,
and Wikipedia. E-mail is the most popular tool among people preferred for sending and receiving
messages quickly. Online banking and shopping is spreading among people from day to day,
social networks like Facebook and Twitter are encouraging people to cooperate by setting the
content of communication and sharing it. As it is usually observed, no matter how old people
are, using digital technologies in the digital era have become a vital need (Goodfellow, 2011).
Individuals using digital tools in the information age have turned into participative and active
individuals who gather, process, and produce information (Sharkey & Brandt, 2008). A digital
literate person uses technology effectively in order to do research, reach information sources,
read-write and comment efficiently, make reasonable choices, and make right decisions. Digital
literacy encourages curiosity and creativity and also enables the individual to evaluate the
information that has been gathered in a critical way. By increasing the ability to use digital
resources, digital literacy helps individuals feel themselves relatively secure at technology usage
(McLoughlin, 2011). However, today still many people are in need of training so that they can
use and manage the multiple and loose information network with the help of technology
(FutureLab, 2010).
Digital literacy is described as creating social mores within individuals’ private lives and the
ability to reflect on this process using digital tools appropriately. Further, digital literacy involves
identifying digital resources and content, reaching, managing, combining, evaluating, and

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making analysis/synthesis, forming new data, creating new ways of media expressions and
making it possible to communicate with others (Martin, 2008). Digital literacy requires multiple
literacy skills (Shariman, Razak, & Noor, 2012). As seen in Table 1, in the field of educational
technology the sub-discipline areas of digital literacy are; computer literacy, technology literacy,
information literacy, media literacy, visual literacy, and communication literacy (Covello, 2010;
Goodfellow, 2011; Simsek & Simsek, 2013).
Digital technologies which people use and are influenced by are multiple, rich, and complex.
Digital literacy is related to learners’ abilities to find and choose reliable as well as relevant
information within complex networks (Gilster, 1997). A digitally literate person knows the most
effective and efficient ways to reach the information he/she needs. That is why, he/she has a
good grasp of ways of searching information. Digital literacy is knowing how to select and use
the digital technologies where, when, and in a purposeful way. Digital literacy is also related to
critical thinking about the opportunities and benefits of digital technologies used frequently
such as Web 2.0, social networks, and mobile applications (McLoughlin, 2011).
Table 1. Subdisciplines of Digital Literacy
Sub-Discipline
Definition
Information Literacy
Finding and locating sources, analyzing and synthesizing the material,
evaluating the credibility of the source, using and citing ethically and legally,
focusing topics and formulating research questions in an accurate, effective,
and efficient manner.
Computer Literacy
An understanding of how to use computers and application software for
practical purposes.
Media Literacy
A series of communication competencies, including the ability to access,
analyze, evaluate and communicate information in a variety of forms including
print and non-print messages.
Communication
Literacy
Learners must be able to communicate effectively as individuals and work
collaboratively in groups, using publishing technologies (word processor,
database, spreadsheet, drawing tools...), the Internet, as well as other
electronic and telecommunication tools.
Visual Literacy
The ability to ‘read,’ interpret, and understand information presented in
pictorial or graphic images; the ability to turn information of all types into
pictures, graphics, or forms that help communicate the information; a group
of competencies that allows humans to discriminate and interpret the visible
action, objects, and/or symbols, natural or constructed, that they encounter in
the environment.
Technology Literacy
Computer skills and the ability to use computers and other technology to
improve learning, productivity, and performance.
From another perspective, digital literacy is the social process of creating the meaning (Future
Lab, 2010). It enables the learners to become active participants in their educational, social,
cultural, and intellectual life. As digital technologies provide opportunities for team work, it
develops the skills to work with different types of people. For example, Wiki web sites encourage
cooperation by allowing the learners to write a text, edit and update it. Google Docs, an online
web based application, enables text based documents to be uploaded whereas it also maintains

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a ground for sharing documents among computers connected to Internet and allows people to
edit texts by teamwork.
Learning Style
Learning style is considered as an important feature for learner preferences. The term of
“learning style” refers to the view that different people learn information in different ways
(Ozata & Ozdamar-Keskin, 2014). It is related to one’s characteristic ways of perceiving,
processing, and interpreting information (Simsek, 2004). There are a number of different
learning style models in the literature. We have chosen Fleming and Mills’s VARK Model and
Grasha and Reichmann’s Learning Style Model to clarify our research objectives.
Fleming and Mills’s VARK (Visual, Auditory, Reading, Kinesthetic) Model (1992) refers to four
learning style dimensions (http://www.vark-learn.com/english/page.asp?p=categories). Visual
learning style (the depiction of information in maps, diagrams, charts etc.), Auditory learning
style (the preference for information that is "heard or spoken."), Reading-writing learning style
(the preference for information displayed as words), Kinesthetic learning style (the preference
related to the use of experience and practice (simulated or real). On the other hand, Grasha and
Reichmann’s Learning Style Model (1996) classifies learning styles by three dimensions and six
learning styles (http://academic.cuesta.edu/wholehealth/disted/about_styles.htm). Avoidant
or participant learning styles (Avoidant students are not enthusiastic about learning content and
attending class while participant students enjoy going to class and take part in as much of the
course activities.) Competitive or collaborative learning styles (Competitive students who learn
material in order to perform better than others in the class while collaborative students enjoy
working with their peers and learning by sharing ideas and talents.) Dependent or independent
learning styles (Dependent students show little intellectual curiosity and who learn only what is
required while independent students who like to think for themselves and are confident in their
learning abilities).
The Purpose of the Study
The purpose of this study is to examine the digital literacy competences and learning habits of
open and distance learners at Anadolu University. Within this context, the research questions
are listed as follows:
1. What are the abilities of the learners to use the digital technologies in digital life?
a. Which digital technologies do the learners have access to?
b. How often do the learners use digital technologies?
c. What are the purposes of the learners in using digital technologies?
2. What are the learning habits of the learners?
a. What are the personal learning preferences of the learners?
b. What do learners think/believe about their own problem solving skills?
c. What do learners think/believe about their own project working skills?
3. What are the abilities of the learners in using digital tools for learning purposes?

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Research Design
Survey methodology was used in this study. The survey consists of four parts: demographic
information, abilities to use digital technologies, learning habits, preferences in using digital
technologies for learning purposes. The survey was developed by Wolverhampton University
Center for Development and Applied Research in Education. After being translated into Turkish,
it was submitted to experts’ opinion and took its final shape after a pilot study. The survey was
issued electronically, was distributed via SMS and e-mail to the open and distance learners in
2012. The data were gathered from 20.172 open and distance learners enrolled in the open and
distance education system of Anadolu University including three faculties as Open Education
Faculty, Faculty of Business, and Faculty of Economics. The number of the participants in the
present study has exceeded the required minimum number (16.406) of respondents needed at
the margin error of 10 percentage points when the population has more than 1.500.000
respondents.
Descriptive data analysis was used for demographic information. The Principal Component
Factor Analysis was applied in order to group and classify the attitudes and statements of the
learners in their personal learning preferences, problem solving skills, project work skills and
abilities to use digital tools for learning purposes. The overall Cronbach’s Alpha reliability
coefficient for the entire scale was calculated to be 0.74. We used Cronbach's alpha to estimate
the scale of consistency among items in the group. The Cronbach's alpha is generally accepted
upon the level of 0.70, albeit it is acceptable at 0.60 in exploratory research (Hair et al., 1998).
Background of Participants
When we analyze the demographic data of the learners (20.172) who participated in the study,
the percentage of male (56%) is not far from the percentage of female (44%) and participants
were mainly in 21-25 (8.266 learners) and 26-35 (6.854 learners) age groups. The majority of the
participants were in their 1st, 2nd and 3rd year of the open and distance learning period. When
the demographic data of the learners were examined by faculty, the majority were in the Open
Education Faculty. Table 2 gives the demographic details of the participants.
Table 2. Demographic Details of the Participants (N=20.172)
Properties
%
Properties
%
Gender
Faculty
Male
56
Faculty of Economics
20
Female
44
Faculty of Business
Administration
35
Age
Faculty of Open Education
45
18-20
8
Year
21-25
41
First year
27
26-35
34
Second year
25
36-45
13
Third year
24
46-55
3
Fourth year
15
56 and above
1
More than 4 years
9
The Survey Design

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The survey used for the digital habits of learners was derived from previous work by Royle and
Hadfield (2012), Hadfield et al. (2009), Claxton et al. (2004), Kay et al. (2009), Kerrigan (2012).
These sources all underpinned the various sections of the survey in an attempt to map the digital
life and habits of learners.
Apart from the biographical Section 2, Section 3 through Section 5 dealt with the issues relating
to the specific sets of values and attributes pertaining to the sources above. These were
supplemented by some questions related to current technologies which had been implemented
since the first questionnaires that were used and designed in previous projects. Section 3
concentrated on learners’ learning orientation, referencing the work of Hunt, Eagle, and Kitchen,
2004 and the work of Claxton et al. (2004) on notions of learning power which includes the
following classifications:
 Resilience: Being ready, willing and able to work through difficulties.
 Resourcefulness: Being ready, willing and able to learn in different ways.
 Reflectiveness: Being ready, willing and able to become more strategic about learning.
 Reciprocity: Being ready, willing and able to learn alone or with other people, using
communication skills and empathy
The purpose of this section was to note the extent to which students preferred working in a way
that was compatible with remote and distance learning and how much support they would need.
In particular, it was expected to provide information on how much knowledge learners have
about the way they like to learn and their knowledge about being an effective learner. This gives
insight into the gaps in students’ knowledge about learning and how they might become better
and more independent learners. It may point to a need for learning designers to focus on the
processes of learning such as reflection on performance and self-evaluation of a range of
learning dispositions. In previous studies, it was noted that students would have a tendency to
over report their abilities in certain areas so it was important to consider any negative responses
in the survey carefully.
Section 4 of the survey was predominantly based on the classification of Next Generation User
Skills (NGUS) as described by Kay et al. (2009). NGUS looked at the needs of industry, learners’
existing digital literacies and the products from existing curricula including 34 sets of ICT
competencies to develop a framework around 5 groups. Two competency groups that represent
the underpinning foundations of personal confidence are assumed to be required by all users:
 Digital Literacy – including safe and social conduct
 Digital Independence – including management of the IT environment
These support three broad and complementary areas of competence:
 Inquiry – including the ability to investigate resources
 Participation – including the ability to collaborate
 Production – including ability to create media”
Thus, Section 4 of the survey used these competencies so that a picture of learners’ capabilities
would emerge that may be vital for designing learning for different cohorts/age groups and
dispositions. Therefore, this section would tell us which skills learners possess in terms of their
use of technology. This allows designers and educators to realize the extent to which learners
actually possess the skills with technology and enables them to consider how those skills can be
acquired or improved. This may also show educators’ practices, knowledge, and skills so that

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students can learn effectively. It is also useful to recognize the skills that students possess so
that these can be used to mentor and coach others.
The final section of the survey combines learners’ non educational use of technology with the
key question of “how they use the technology they have”. This section was derived from
previous questionnaires and from the work of Kerrigan (2012) and the Digital Literacies in
Transition Project. This section focuses on the use of technologies for a variety of purposes
including studying and learning. In particular, this section provides insights into what learners
are engaging with digitally at home and when not studying. This should also give insights into
which platforms can be used to reach users although there are ethical issues about invading
learners’ space that need further investigating. However, great inroads have been made in this
area, particularly in the use of Facebook for learning but this should be tempered by a
responsibility to engage learners thinking in regard to an NGUS area, such as maintaining and
protecting their digital identity online and safety. For educators and designers, an awareness of
their learners’ digital habits should allow them to construct learning events that transfer those
habits into institution derived formal digital learning.
Findings and Discussion
Learners’ Abilities to Use Digital Technologies
In this section, findings regarding open and distance learners’ ownership of information and
communication technologies and their frequencies of usage as well as their purposes of using
technology are presented.
Ownership of Information and Communication Technologies
The majority of the respondents have personal computers at their houses (92%). However, it
appears that 36% of the computers are used only by the learners and 46% of the computers are
located in the learners’ own room. In other words, it has been observed that most of the learners
who have personal computers share their computers with other family members. When it
comes to the Internet connection used in the houses, 54% preferred wireless network and it was
observed that 12% owned a personal computer without Internet connection at their house.
When other digital tools are in question, mobile phones with internet access (55%), laptops
(48%) and desktops (46%) are the most common ones that the learners possess. These tools are
followed by digital cameras (44%), smart phones (28%) and iPod or MP3 players (24%). The
percentage of the respondents having a mobile phone with internet access (55%) is higher than
the percentage of the respondents having a smart phone (%28) and the percentage of the
respondents having a mobile phone without Internet access (20%). Further, it is seen that the
percentage of holding a game console (10%), netbook (9%), tablets (6%) and e-book reader,
kindle or nook is comparatively low. The ownership of proper technology is an important issue
and this situation may create serious challenges in term of improving learners’ digital literacy
skills.
The Frequency of Using Technology and the Purposes

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Most of the respondents stated that they spend around 2-3 hours in a day online. Among the
participants 17% spend between 4-6 hours online and 15% spend more than 6 hours a day.
Figure 1 presents the use of digital tools and respondents’ purposes in using digital tools.
Figure 1. Participants’ Habits of Using Digital Tools
Respondents’ purposes in using current web sites and digital tools were also evaluated.
However, the web sites and digital tools in question are not commonly used by them. It was
seen that digital tools are mainly used for social purposes or within the frame of personal
interests. Music web sites, mobile applications, Twitter, YouTube, e-mail and Gittigidiyor (a
popular shopping website in Turkey such as eBay) are the top tools of the learners used for these
purposes. It is observed that these tools are used rarely for the purpose of education. The
learners with the study/learning purpose preferred, by order, e-mail, mobile applications,
YouTube and other video sharing web sites.
Learning Habits
In this section, open and distance learners’ personal learning preferences, problem solving skills,
and project working skills are analyzed.
Personal Learning Preferences
Five factors were obtained as a result of the factor analysis carried out for the purpose of
identifying the personal learning preferences of the learners (Table 3). The alpha values of the
first three factors are 0.716, 0.714 and 0.732 respectively, representing high internal consistency
of these components. However, the alpha values of the fourth ( = 0.512) and the fifth ( =
0.347) factors are quite low, due to the very limited number of items. These five factors explain
57% of the total variance.
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Blackberry Messenger
Google Talk
Whats App
Yahoo Messenger
Gittigidiyor
e-Mail
Blog / Vlog
Facebook
LinkedIn
Google+
Mailing Lists
RSS subscription
Slideshare
Moodle
Flickr/Instagram etc..
YouTube or other video…
Wikis
Dropbox
Twitter
Mobile Apps
iTunes
Other music sites
Podcast/Vodcast
Not Use Networking Research Work Study/Learning Personal Interests Social Purposes

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The first factor represents the learners’ “visual learning style” and it has 4 sub items. This factor
points out the attitude where students remember the graphics and visuals more than something
they listen or read and they prefer to get help from the graphics and visuals while learning new
things. The percentage of the variance which is explained in the factor analysis displays the
relative importance of the factors (Altunisik et al., 2012). According to this first factor “visual
learning style” explains the biggest part of the variance with a ratio of 23%. This preference
includes the depiction of information in maps, diagrams, charts, graphs, flowcharts, labeled
diagrams, and all the symbolic arrows, circles, hierarchies and other devices that people use to
represent what could have been presented in words. (Fleming & Mills, 1992; Hunt, Eagle, &
Kitchen, 2004). Using game/simulation, picture and graphics during the learning process are the
important elements observed in this factor.
The second factor represents students “auditory learning style” and it has 4 sub items. This
factor refers to the attitude where students prefer listening to the things that are being told
instead of reading. “Auditory learning style” explains 11% of the total variance. This perceptual
mode describes a preference for information that is “heard or spoken” (Fleming & Mills, 1992).
The item with the highest ratio within this factor is to prefer listening to an instructor instead of
reading the course materials.
The third factor represents the “dependent learning style” and it has 2 sub items. Dependent
learning refers to an expert guided course preference. Students who prefer dependent learning
think that education is finding the “right answer” and this information is perceived to be held by
the lecturer (Grasha & Reichmann, 1996; Hunt, Eagle, & Kitchen, 2004). The factor in the study
covers the students’ preference to work with an expert and face to face and explains 8% of the
total variance. The mean of these items shows that learners are concerned about studying with
an expert face to face.
The fourth factor represents learners’ “collaborative learning style” that is a preference of
working collaboratively rather than working alone and it has 4 sub items. It covers the facts like
learning within a group or doing collective work (Grasha & Reichmann, 1996). This factor
explains 8% of the total variance. The item with the lowest ratio within this factor is desire to do
a collective work online.
The fifth and the last factor represents the “reading and writing learning style” and it has 2 sub
items. Within this factor, the attitudes of the learners showing their preference on reading the
written materials related with the course rather than listening are stated. This factor explains
6% of the total variance. However, the answers students have given demonstrate that choice of
learning by written material is not very high.
Table 3. Factor Analysis Results for Personal Learning Preferences (N=20.172)
Items
M
SD
Factors

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1
2
3
4
5
1st Factor: Visual Learning Style
I prefer to obtain new information by
figures or graphics rather than written
or oral means.
2,83
,81
,775
I remember better the things that I see
on pictures or on graphics more than
the things I listen or read.
2,89
,79
,766
I like to use game and simulation in
learning.
2,94
,80
,652
I draw simple graphics, figures or charts
to summarize the course subjects.
2,79
,77
,651
2nd Factor: Auditory Learning Style
I prefer listening to reading.
2,70
,83
,755
I understand better if someone tells me
the subject instead of reading.
2,82
,86
,707
I remember better the things I heard
rather than the things I saw.
2,40
,81
,669
I prefer listening to the instructor rather
than reading the documents.
3,02
,82
,632
3rd Factor: Dependent Learning Style
I prefer to study face to face.
3,13
,74
,815
I prefer to study with an expert.
3,28
,72
,797
4th Factor: Collaborative Learning Style
I prefer to study within a group or with a
friend.
2,79
,83
,738
I prefer to study individually/
independently.
2,74
,83
,720
I learn by watching other people.
2,69
,79
,538
I prefer to do a collective study online.
2,52
,81
,452
5th Factor: Reading-Writing Learner
Style
I find more useful a written summary of
a class compared to a one done by
figures or orally.
2,99
,82
,726
I remember better the things that are
written rather than the told ones.
2,66
,88
,589
Corevalue
3,722
1,788
1,383
1,286
1,033
Declared Variance (%)
23,263
11,173
8,645
8,039
6,455
Cronbach Alfa
,716
,714
,732
,512
,347
Note: 1=Definitely not agree, 4= Definitely agree.
When all these factors are evaluated together by observing the mentioned variance ratios, it can
be stated that students prefer learning by graphics/visuals and by listening compared to the
other methods. The percentage ratio of the variance (57%) explained by these five factors can
be mentioned as high. Comparably, the study of Ozata and Ozdamar- Keskin (2014), which
examined learning orientations of 168 business school students at Anadolu University, has
produced the same five factors as visual, auditory, dependent, collaborative, and reading-
writing skills.

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Problem Solving Skills
Three factors have been obtained as a result of the factor analysis which is conducted to
understand the beliefs of the students related with problem solving skills (Table 4). These 3
factors explain 65% of the total variance. The alpha values of the first two factors are acceptable
(0.781 and 0.670) and represent high internal consistency of these components. However, the
alpha value for the third factor cannot be calculated due to the inadequate number of items
(only 1 item).
The first factor is named as “working on a problem”. The 4 sub items of this factor cover
statements about identifying the problem and solving it. “Working on a problem” as being the
first factor explains the biggest part of the total variance which is 41%. “Asking the right
questions to solve the problem” and “gathering the necessary data” are the items that have the
highest means within this factor.
Second factor is named as “evaluating the solution methods”. The 3 sub items within this factor
cover student attitudes towards evaluating the alternative solution methods, solving the
problem without giving up, and evaluating their progress. “Evaluating the solution methods”
being the second factor explains 12% of the total variance. Within this factor, item “not giving
up easily” got the highest mean.
The third and the last factor is named as “avoid solving a problem”. Only one item takes place
within this factor and it points out students’ trust in their friends about problem solving. This
factor explains 11% of the total variance. However, the answers that the students have given
show that the ratio of third factor is not very high.
When all these factors are evaluated together by observing the variance percentages which are
indicated, the factor “working on a problem” is a primary issue when compared to other factors.
The percentage the variance explained by these three factors (65%) can be considered as high.
Table 4. Factor Analysis Results for Problem Solving Skills (N=20.172)
Items
M
SD
Factors
1
2
3
1st Factor: Working on a problem
I am good at identifying the problems that
arise.
2,94
,66
,775
I can use the necessary information in order to
solve a problem.
3,05
,66
,761
I can use a systematic method in order to solve
a problem.
2,77
,75
,728
I can ask the right questions in order to solve,
learn and understand a problem.
3,00
,67
,718
2nd Factor: Evaluating the solution methods
I constantly evaluate my progress to be sure
about the method that I am following.
3,08
,76
,803
I generally think of more than one solution
method in order to do something.
3,12
,70
,731

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I do not give up easily while working on a hard
task.
3,18
,77
,685
3rd Factor: To avoid solving a problem
I generally trust my friends in solving problems.
2,09
,69
,991
Corevalue
3,331
1,000
,917
Declared Variance (%)
41,638
12,502
11,468
Cronbach Alfa
,781
,670
-
Note: 1=Definitely not agree, 4= Definitely agree
Project Work Skills
Three factors were obtained as a result of the factor analysis carried out for the purpose of
identifying the project work skills of the students (Table 5). All the alpha values (0.849, 0.773
and 0.736) of these factors are above the level of 0.70. These three factors together explain 71%
of the total variance.
The first factor is named as “planning”. The 3 sub items of this factor cover the statements about
planning things which have to be completed before starting a project. “Planning” being the first
factor explains 50% in total variance. Setting a target and time management are the items that
have the highest ratio within this factor.
The second factor is named as “project management”. The 3 items within this factor cover the
statements of the students about people expressing themselves within the frame of project
management and reaching the information that is required. “Project management” as a second
factor, explains 11% of the total variance. Within this factor, “maintaining a ground for sharing
the ideas freely” is the item with the highest ratio. However, other items within this factor have
ratios close to one another.
The third factor is named as “evaluating the results”. The 3 items within this factor identify the
evaluation process after a work has been completed and getting the feedback. This factor
explains 9% of the total variance. Item having the highest ratio within this factor is about
students’ thinking over to evaluate themselves after a work is completed.
When all these factors are evaluated together by observing the variance percentages that are
indicated, the factor “planning and setting a target” appears to be the main issue when
compared to other factors. The percentage of the variance explained by these three factors
(71%) can be stated as pretty high.
Table 5. Factor Analysis Results for Project Work Skills (N=20.172)
Items
M
SD
Factors
1
2
3

CONTEMPORARY EDUCATIONAL TECHNOLOGY, 2015, 6(1), 74-90
86
1st Factor: Planning
Before I start studying I first think about what to
do and make a plan.
3,26
,64
,863
I set a target for my tasks and make a time table.
3,39
,61
,807
I overview the every phase of my work.
3,23
,62
,787
2nd Factor: Project Management
In a group study I ensure that everyone has the
necessary information they need.
3,18
,62
,792
I do what is necessary and make sure that people
can express their ideas freely.
3,29
,61
,784
In a group study I make sure that each person is
suitable for the work he/she is doing.
3,20
,62
,743
3rd Factor: Evaluation of the Results
I like to get feedback about the work I had done.
3,38
,67
,874
Once a task is over, I think over the things I have
done good and the things I need to improve.
3,46
,60
,684
In order to reach a target I can prioritize the
works that have to be done according to their
importance.
3,33
,61
,540
Corevalue
4,547
1,051
,823
Declared Variance (%)
50,525
11,677
9,143
Cronbach Alfa
,849
,773
,736
Note: In the scale that has been used 1=Definitely do not agree, 4= Definitely agree
Ability to Use Digital Tools For The Purpose of Learning
Four factors have been obtained as a result of the factor analysis that was conducted to
understand the learners’ abilities to use digital tools toward learning (Table 6). All the alpha
values (0.897, 0.924, 0.897 and 0.860) of these factors are above the critical level of 0,70 and
represent a very high internal consistency.
Table 6. Factor Analysis Results for Ability to Use Digital Tools for the Purpose of Learning
(N=20.172)
Items
M
SD
Factors
1
2
3
4
1st Factor: Ability to use digital learning tools
I can comment, suggest and evaluate things
online.
3,15
0,70
,817
I can join to the events on social networks.
3,18
0,71
,815
I can use applications like Google Docs that are
open for sharing.
3,21
0,68
,759
I can work online for a purpose with cooperation.
2,98
0,80
,709
I can create profiles on social media and manage
them.
2,75
0,99
,663
2nd Factor: Managing digital learning platforms
I can do adjustments on digital platforms (visual
or audio).
2,61
0,91
,804

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Note: 1=Definitely do not agree, 4= Definitely agree
As seen in Table 6, these four factors together explain 70% of the total variance. The first factor
is named as “ability to use digital learning tools”. The 5 sub items of this factor are about the
capabilities of using digital tools for basic learning purposes. The first factor, “ability to use digital
learning tools” explains the biggest part of the total variance with 51% .Using Google documents
and social networks are the two items that have the highest means under this factor.
The second factor is named as “managing the digital learning platforms”. The 6 sub items of this
factor cover the attitudes of the learners’ abilities to use digital platforms at basic level.
“Managing the digital learning platforms” explains 10% of the total variance. Uploading files to
digital platforms and using significant applications are the two sub items with the highest means.
Third factor is named as “ability to use advanced level digital tools”. The 7 sub items of this factor
include the statements about learners’ abilities to use digital platforms at an expert level. This
factor explains 6% of the total variance. The item with the highest mean within this factor is
about learners’ acting as a moderator for an online group. The low means of the items within
the second and the third factors can be considered as an indicator showing how limited the
learners’ abilities to use digital technologies are.
I can upload files (visual or audio) to digital
platforms.
2,78
0,90
,781
I can use several applications and contents for
one purpose.
2,74
0,85
,737
I can form digital objects (figures or digital
designs).
2,35
0,93
,727
I can use the version controls of the digital
objects.
2,41
0,98
,671
I can publish the digital content (in platforms like
WEB, PDF, e-book, blog or video).
2,54
0,98
,658
3rd Factor: Ability to use advanced level digital tools
I can use Google Adsense tool.
2,07
0,94
,783
I can make a web list on Google.
2,08
0,95
,715
I can organize advertising campaigns by using
several online platforms.
2,08
0,95
,710
I can write a QR code and manage it.
1,63
0,81
,688
I can create an application.
1,78
0,87
,666
I can use Twitter hashtags.
2,21
0,96
,656
I can act as a moderator in online groups.
2,36
0,96
,567
4th Factor: Security and ethics
I can stay online in a secure way.
2,65
0,93
,770
I know that I have a social responsibility to act in
an ethical way in online platforms
3,12
0,75
,715
I know the digital rights of ownership.
2,74
0,87
,665
I can fix my way of communicating according to
different target recipients.
2,84
0,83
,539
Corevalue
11,306
2,154
1,349
0,850
Declared variance (%)
51,392
9,790
6,131
3,865
Cronbach Alfa
0,897
0,924
0,897
0,860

CONTEMPORARY EDUCATIONAL TECHNOLOGY, 2015, 6(1), 74-90
88
The last factor about using digital technologies is the aspect of “security and ethics”. The 4 sub
items of this factor cover the statements related with doing secure operations in digital
platforms and also the right of ownership and ethical behaviors. This factor explains 4% of the
total variance. Depending on the means of the answers about these items which learners have
replied, it can be said that they do not know much about this issue.
When these factors are evaluated all together depending on the variance ratios, the first factor
“managing the digital learning platforms” has more priority compared to other factors. It can be
stated that the variance which is explained by these three factors within factor analysis has a
70% share, which can be considered pretty high.
Conclusions and Recommendations
When the digital literacy competence of open and distance learners is analyzed, it can be said
that they have the basic competences of digital literacy. It has been pointed out that learners
have the skills to use information and communication technologies at a basic level. They need
training on how they can use the digital tools more efficiently for the purpose of learning. As
McLoughlin (2011) stated, there is still training need for learning on digital literacy skills and
digital competencies.
Results of the learning habits can be summarized as follows:
 Participants generally prefer learning through graphics or visuals, through listening, and
through written materials. According to Fleming and Mills (1992), these choices are
conceptualized as visual, auditory, and reading-writing learning styles.
 Majority of participants are dependent learners and they prefer face to face education.
According to Grasha and Reichmann (1996), they enjoy authority figures for specific
guidelines on what to do.
 Participants are usually not competitive learners. According to Grasha and Reichmann
(1996), competitive learners enjoy competing with other students in a course for the
rewards that are offered, rather than learning collaboratively within a group (Kay et al.,
2009).
 Most of the participants consider having problem solving and project working skills to
deal with educational difficulties. We can say that they have inquiry skills including the
ability to investigate resources (Kay et al., 2009).
It is observed that the ratio of using personal computers is high but the use of new generation
technologies like smart phones and tablets are not common among the learners. This situation
may be due to the fact that the prices of the new generation devices are high. Together with
this, it may also rise from the fact that necessity to use these devices in educational settings is
not created. Anadolu University’s mobile learning and interactive e-book services that have
occurred within the context of e-learning transformation may cause a necessity and encourage
the learners to use new generation information and communication technologies. Learners’
adaptation to new generation technologies is important for them to form new learning habits
like learning anywhere and anytime. Otherwise in the near future, facing disparity among the
learners and this leading to a digital gap will be inevitable (Goodfellow, 2011).

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It is observed that the learners have the capability of- including skills like- solving problems,
working in cooperation for projects, trusting themselves in communicating, reaching the
information, analyzing it, evaluation and transmitting it. In terms of designs, they prefer visual
learning platforms and by this way they have the ability to read, comment, and understand the
information which is given with pictures and graphics. Their apparent dislike of collaborative
online group work may be problematic as this is often a way in which modern businesses work.
This may provide a unique opportunity to promote collaboration in education to support
employability. Taking into account all of these, further research is needed to explore how to
increase the use of digital tools efficiently for the purpose of learning and also how to design
learning environments to improve distance learners’ digital literacy.
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Correspondence: Nilgun Ozdamar-Keskin, Assistant Professor, Department of Distance
Education, Open Education Faculty, Anadolu University, Yunus Emre Campus, Eskisehir, Turkey