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Learning Preferences and Learning Styles of Online Adult Learners



Learning preferences and learning styles are a way to enhance the quality of learning, especially for those who are alone in front of a computer, i.e. online students. Any student can adapt learning processes, activities and techniques, if he/she is able to understand his/her own personal characteristics and the consequences of possible different experiences. In order to help students to learn better, instructors should provide an easy way for them to make discoveries about themselves. Thus, the aim of this chapter is "to reveal possible learning preferences and learning styles of students in online environments" and "to provide sample ideas for e-instructors" to address these diverse learners. For this purpose, qualitative research methods will be used. The literature will be reviewed and students will be posed several open-ended questions about their learning preferences. Based on the findings, learning preferences and learning styles will be grouped in a general way, and e-instructors provided with examples of instructional media, materials and methods.
Learning Preferences and Learning Styles of Online Adult Learners
Yasemin Gülbahar1 Ayfer Alper2
1 Distance Education Center, Ankara University, Gölbaşı, 06830 Ankara, Turkey
2 Faculty of Educational Sciences, Ankara University, Cebeci, 06590 Ankara, Turkey
Learning preferences and learning styles are a way to enhance the quality of learning, especially for those who are alone in
front of a computer, i.e. online students. Any student can adapt learning processes, activities and techniques, if he/she is
able to understand his/her own personal characteristics and the consequences of possible different experiences. In order to
help students to learn better, instructors should provide an easy way for them to make discoveries about themselves. Thus,
the aim of this chapter is “to reveal possible learning preferences and learning styles of students in online environments”
and “to provide sample ideas for e-instructors” to address these diverse learners. For this purpose, qualitative research
methods will be used. The literature will be reviewed and students will be posed several open-ended questions about their
learning preferences. Based on the findings, learning preferences and learning styles will be grouped in a general way, and
e-instructors provided with examples of instructional media, materials and methods.
Keywords learning preferences, learning styles, e-learning, online instruction
1. Introduction
E-learning tools and techniques have dramatically changed the higher education process in recent years. As reiterated
by Paechter and Maier (2010), “Over the past few years, digital media have enriched the teaching and learning
experiences and have become commonplace with university students and lecturers.” (p. 292). Although the applications
of e-learning at universities have increased rapidly, little is known about learners’ preferences and styles in online
environments. Do any adult learning styles or preferences for a face-to-face environment carry over into an online
environment? This chapter is written in order to shed light on this question in depth. The need for research on this
phenomenon is also underlined by Zheng, Flygare and Dahl (2009) who stated, “The increasing application of web
technology has demanded that an understanding must be made in regard to learners’ cognitive styles and their
interaction with different instructional strategies in online learning.” (p. 222). Thus, the design, development and
management of online instructional programs are of critical importance, since our main purpose is to present learners
with effective and efficient environment that enhances learning.
As we are all aware, “There is no one preferred learning style that works for all students or even for any one
particular ethnic or cultural group” (Arp & Woodard, 2006). Every student learns in her/his own way, which has
popularized the approach of “personalized learning” in recent years. Hence, personalization of learning means dealing
with adults’ learning preferences, learning styles and the behaviors. Instructors are therefore facing difficulties while
transferring their know-how and valuable experiences from traditional environments to online environments. One of the
main difficulties in this transfer process is recognizing the individual differences among learners. Therefore, identifying
learning styles is a critical step in understanding how to improve the learning process (Hamada, Rashad & Darwesh,
Research has shown that if the instruction is delivered in the preferred styles of a student, an increase in motivation
and achievement can be observed (Fahy & Ally 2005; Manochehri & Young, 2006; Bezalel & Barth, 2007). Since there
is a probability of increasing success in the performance, “... students should have an opportunity to better understand
their own learning styles and accordingly what kind of curriculum delivery best fits their style.” (Rogers & McNeil,
2009, p. 10). There are many research studies which take into account learning styles in e-learning settings, “But while
they have pointed to possible associations between style and ICT use, they have not explored the nature of the
interaction between learning style and ICT use.” (Heaton-Shrestha, Gipps, Edirisingha & Linsey, 2007, p. 443). In fact,
there is a real need to explore the effects of considering e-learning styles in depth when considering different variables
in online environments.
2. So what are Learning Preferences and Learning Styles?
Learning styles have really gained so much attention in recent years across different age groups and learning
environments. Rayner (2006) also underlines this fact: ‘a heady mix of metaphor, sound bites and polemic … an
academic and political debate in which far more heat than light is generated’ (p. 5). Thus, “The area of learning styles is
complex and many questions are still open, including a clear definition of learning styles, a comprehensive model which
describes the most important learning style preferences, and the question about the stability of learning styles”
(Kinshuk, Liu & Graf, 2009, p. 740). As stated by Felder and Silverman (1988), grouping students according to a
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number of scales pertaining to the ways they receive and process information is defined as a learning style model.
Similarly, according to Jonassen and Grabowski (1993), learning styles are tendencies for the preference to process
information in certain ways.
In another definition, a learning style can be described as the composite of cognitive, affective, and psychological
characteristics that serve as an indicator of how an individual interacts with and respond to the learning environment
(Keefe, 1979; Duff, 2000). In other words, learning styles can be described as the means of perceiving, processing,
storing, and recalling attempts in the learning process (James & Gardner, 1995). Various cognitive and learning style
theories and models have been proposed over the course of many years, identifying and categorizing students’
individual differences like Hill’s Cognitive Style Mapping (1976), Dunn and Dunn Learning Styles (1978), Howard
Gardner’s Multiple Intelligence Theory (1983), Kolb’s Learning Styles (1984), Gregorc Learning Styles (1985), Felder-
Silverman Learning Model (1988), Grasha-Reichmann Learning Style Scales (1996), and Hermann Brain Dominance
Models (1996). These models of learning styles are currently being used to assess how students learn.
According to Kolb (1984), individuals learn in four stages or modes: Concrete Experience (CE, e.g. laboratories,
field work, observations), Reflective Observation (RO, e.g. journals, logs, brainstorming), Abstract Conceptualization
(AC, e.g. papers, lectures, analogies), and Active Experimentation (AE, e.g. simulations, case study, homework).
However, the process of constructing knowledge in different learning situations involves a creative combination of the
four learning modes that is responsive to contextual demands. The combination of learning modes used to establish the
four quadrants reflect the four learning styles: Accommodators (favored CE and AE, i.e. feeling and doing), Divergers
(favored CE and RO, i.e. feeling and watching), Assimilators (favored AC and RO, i.e. thinking and watching), and
Convergers (favored AC and AE, i.e. thinking and doing).
The Grasha-Reichmann Learning Style Scale is a 60-item self-evaluation inventory that utilizes a five point Likert
scale and represents six learning styles (1996). This inventory assessed the learning styles of college students through a
social, affective perspective on the different ways individuals approach the classroom environment (Keefe, 1979). The
Gregorc learning style is based on a bidimensional level between perception and ordering. Perceptual preference refers
to acquisition in either an abstract or concrete manner, or in some combination. Abstract perception refers to the ability
to process information through reason and intuition, often invisible to our physical senses. Concrete perception refers to
the ability to process the physical aspects of information through the senses (Jonassen & Grabowski, 1993). The Index
of Learning Styles (ILS) (Felder & Soloman 1991) is a 44-question survey based on a learning style model formulated
in 1988 by Richard M. Felder and Linda K. Silverman. It was further developed by Richard M. Felder and Barbara A.
Soloman in 1991. Of the eight dimensions defined by the index, four of them appear to describe learning styles that are
reflective, intuitive, verbal, and global.
Given the above-mentioned list of assumptions derived from the literature, one of the most popular models for
learning styles is the Felder-Silverman Dimensions of Learning Style model. Felder and Silverman developed their
learning style model based on a composite of several theories (e.g., Jung’s theory of psychological types, information
processing). The model combines several dimensions presented in the Myers-Briggs model (Sensing/Intuitive) with
Kolb’s information processing dimension (Active/Reflective). It also avoids the complexity of the Dunn and Dunn
model (Moallem, 2007). Nevertheless, the most common classifications of learning styles in the field is Witkin’s
cognitive style field dependence and independence model and Kolb’s learning style model that classified individuals’
learning profiles as accommodator, diverger, converger, and assimilator (Desmedt & Valcke, 2004).
Although there are numerous learning style groupings and models proposed by many researchers, to summarize all of
them is not in the scope of this paper. So, selections of them are briefly listed here in order to give an idea about the
3. Differentiating ‘Face-to-Face’ and ‘Online Environments’
In their study, Paechter and Maier (2010) tried to reveal the aspects of e-learning courses that students experience as
being favorable for learning, as well as students’ preferences about online or face-to-face learning components. Their
study indicated that students preferred online learning, “… for its potential in providing a clear and coherent structure of
the learning material, in supporting self-regulated learning, and in distributing information” (p. 292), whereas, “They
preferred face-to-face learning for communication purposes in which a shared understanding has to be derived or in
which interpersonal relations are to be established” (p. 292). Since learners prefer different learning environments for
different purposes, it is worth investigating the factors that may show diversity between different learning
Heaton-Shrestha, Gipps, Edirisingha and Linsey (2007) conducted a study in order to address the issue of whether
student learning style has an impact on the use of learning technologies such as a virtual learning environment (VLE).
The researchers concluded that, “…with respect to the question of whether ICT use changes style or style changes ICT
use, we found that style shaped use, as learners tended to use the VLE in a manner consistent with their preferred style.
However, in some cases, students used the VLE to deliberately change their style and approach.” (p. 461). Comparing
student satisfaction in online vs. face-to-face instruction, the researchers postulated that learning style is influenced in
distance education (eg. Eastmond 2000; Soles & Moller 2001; Offir, Bezalel & Barth 2007; Mehlenbacher et al. 2000;
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Angeli & Valanides, 2004; Fahy & Ally, 2005; Manochehri & Young, 2006) . However, other researchers (see, e.g.,
Ahn & Ahn, 2000; DeTure, 2004; Dille & Mezack, 1991; Ingebritsen & Flickinger, 1998; Neuhauser, 2002) have not
found such relationships between learning style and success in online learning (Battalio, 2009).
As recently as 2009, The Department of Education conducted a meta-analysis of research between 1996 and July of
2008. Their findings suggest that students who took all or part of their class online performed better, on average, than
those taking the same course through traditional face-to-face instruction (Gebara, 2010). Summers, Waigandt and
Whittaker (2005) similarly stated that students who may not have adopted proper strategies for self-regulation may be
faced with obstacles and drop out of the course, which in the end results in higher rates of attrition than face-to-face
In their research study, Beadles II and Lowery (2007) assessed whether students’ learning styles can predict the
choice of educational delivery or not. Their findings revealed differences in learning styles between students who chose
to enroll in a traditional program and those who chose a web-based program. The researchers concluded, “… that
learning styles might be an important determinant of the choice of educational delivery method” (p. 110). The virtual
world of learning, together with the tools and technologies implemented, has definitely changed the method of delivery,
but it has not changed the ultimate goal of learning. Since today’s technologies allow educators to act in a multi-modal
learning environment, the learning style needs of all students are afforded the opportunity to do well (Gülbahar, 2005a;
Whiteley, 2007). These studies, therefore, signify that online learning environments should be designed and
implemented to accommodate individual differences and there is much more research needed in order to clarify the
4. The Importance of Learning Preferences and Learning Styles
Learning style has been shown to play an influential role in students’ reactions to a Web-based instructional program,
with students exhibiting different cognitive styles showing varying preferences with respect to the features of TML
(Chen, Chen, & Xin, 2004). Hsieh and Dwyer (2009) examined the instructional effectiveness of different online
reading strategies for students identified as possessing different learning styles, either internal or external locus of
control styles, on tests measuring different learning objectives. The researchers concluded that, “... different reading
strategies have different instructional structures and functions in facilitating student achievement of different types of
learning objectives” (p. 47).
Battalio (2009) conducted a study in order to determine the extent to which student learning styles are associated
with success in online learning environments, particularly when controlling the amount of collaboration available to
students. As concluded by the researcher, “The results of this study have shown significant associations between
students’ learning styles and success in distance education and offer insight into the relationship between learning style
and mode of delivery” (p. 83). Lightner, Doggett and Whisler (2010) also stated that, “Students in an online program
must be more resourceful because they do not have immediate access to instructional and technical resources and are
called upon to make decisions without instant corroboration; hence, learning style inventories may be of value for
measuring this attribute and predicting success in such an environment.” (p. 8).
Graf, Kinshuk and Liu (2009) proposed an automatic approach for identifying students’ learning styles in the
Learning Management System, which is based on inferring students’ learning styles from their behavior in an online
course. They concluded that the information about students’ learning styles can be used for; (a) providing teachers with
more information about their students, showing them that their students have different preferences and ways in which
they learn, (b) helping teachers in understanding why and when students may have difficulties in learning, and (c)
making students themselves aware of their own learning styles, helping them to better understand their strengths and
weaknesses in the learning process. Topçu (2008) also conducted a research study which took into account the learning
styles of the participants and verified the efficacy of the intentional repetition technique in improving interaction in
asynchronous online discussions. Furthermore, this researcher stated that, “... instructors’ awareness of the learning
styles and cultural context may be helpful for increasing students’ performance in web-based learning environments”
(p. 916).
Franzoni and Assar (2009) developed an integrated taxonomy combining learning styles, different teaching strategies
and the corresponding appropriate electronic media. The researchers’ goal was to provide a structured method to help in
facilitating the learning process and personalizing the pedagogical resources. Hence, they concluded that, “The
presented taxonomy is thus a useful tool to get a better knowledge of the wide variety of resources available to use in
class” (p. 28). Coole and Watts (2009) investigated communal e-learning styles in online classrooms; they proposed that
their study highlighted the need for multiple pathways in web-based provision for trainees on such courses to meet
individual, preferred, learning styles. The researchers also concluded that, “While the context of this study focuses on
the VLE, given the rapid developments in ICT people are now gaining knowledge informally using other forms of
technology” (p. 23).
In the e-learning environment, students with social, aural, verbal, and solitary learning styles have high academic
achievement respectively. Students with logical and physical styles have the least academic achievement (Kia et al.,
2009). Ramayah et al. (2009) highlighted that female students were found to demonstrate slightly higher preference for
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the V(isual) and A(ural) learning styles when compared to male students. Lindsay (1999) found that the harmony
between learning style and teaching style increased academic achievement and satisfaction with learning.
Since learning styles provide information about individual differences in learning preferences, they are able to
indicate how instruction can be best designed to support learning preferences and increase academic achievement
(Akdemir & Koszalka, 2008). Whisler (2005) recommends that students considering online courses should assess their
self-efficacy, interaction behaviors and time management skills to see to what extent online programs are suitable for
them. As observed in the literature, knowing about self-capabilities may lead an individual to perform better on the
required tasks. Since e-learning is mainly based on self-control and self-regulation, learners’ awareness of their own
capabilities is now much more important than ever.
5. Possible Preferences in Online Environments
Li, Leh, Fu and Zhao (2009) conducted action research to reveal learners’ preferences while using online resources and
ended up with eight preferences. Their findings indicated that learners mostly preferred using resources from the course
web site rather than other sites, preferred using online resources and online syllabus rather than printed ones, preferred
text-based FAQs rather than those in video format, preferred a teaching sequence which conforms to their individual
sequences and preferred the shared use of online resources.
Kinshuk, Liu and Graf (2009) investigated the interactions between students’ learning styles, behavior, and
performance in an online course which did not match their learning styles. Their findings showed that, “… learners with
strong preferences for a specific learning style have more difficulties in learning, in terms of achieving lower scores,
than learners with mild learning style preferences” (p. 750).
Johnson (2007) investigated the impact of learning style on college student preference for and achievement under
two specific web-based instructional conditions—quizzes and study groups. They found that different learners had
different preferences, for instance active learners expressed a preference for face-to-face study groups rather than web-
based study groups, whereas visual learners expressed a preference for web-based quizzes rather than web-based study
groups. The researcher also validated these preferences by indicating decreased academic achievement under the less-
preferred study condition. Hence, based on her findings, Johnson (2007) concluded that, “Instructional applications of
web-based technology may provide mechanisms to accommodate student learning style more consistently in higher
education.” (p. 630).
Saeed, Yang and Sinnappan (2009) conducted action research to validate their research framework which, “… is
based on the fact that learners’ individual characteristics influence their preferences for using technology and that the
use of appropriate technology positively influences the academic performance” (p. 100). The researchers finally
underlined the significant relationships between students’ learning styles and technology preferences and their impact
on academic performance. Akkoyunlu and Soylu (2008) also revealed that, “... students’ views on the blended learning
process, such as ease of use of the web environment, evaluation, face-to-face environment, etc., differ according to their
learning styles” (p. 183). Terrell and Dringus (2000) and Lippert, Radhakrishnam, Plank and Mitchell (2001) measured
learning styles of online learning students with a high level of computer literacy, based on the Kolbs's LSI. Both studies
showed that learning style had no effect on success in online learning but it determined the preference for this delivery
format. For instance, students who fell into the Converger and Assimilator learning styles felt more comfortable taking
distance learning courses.
One of the questions, whether there is a relationship between the subjects’ learning style and preferred method of
instruction, is an important one to understand when designing online courses. Jiang and Ting (1998) evaluated students’
perceived learning in an online course and they found that the more interactive the course design, the greater the
students’ perceived learning and the more interactive the professor, the more the students participated. These findings
support those found by Swan et al. (2000), who suggested that, the more interactive the instructor, the higher the student
satisfaction. In addition, students also felt that more learning occurred if instructor interaction was high. Furthermore,
they mentioned that students had a higher level of satisfaction and learning, as demonstrated by courses that had higher
instructor interaction, interactions with classmates and higher levels of activity.
Butler (2004) studied students’ learning styles and their preferences for online instructional methods. The results
revealed that there were several significant positive and negative relationships between learning styles and instructional
methods. For example, the concrete sequential style exhibited a positive relationship with e-mail use, the concrete
random style revealed a negative correlation to online examinations, while the Abstract sequential type demonstrated a
positive relationship with computer simulations, but a negative one to the use of multimedia (p. 106).
Saeed, Yang and Sinnappan (2009) concluded in their study that, “’s learners are flexible in stretching their
learning styles and are able to accommodate varying instructional strategies, including the use of emerging web
technologies” (p. 106). Saeed, Yang and Sinnappan (2009) also suggested that the learning styles of today’s learners are
flexible enough to experience varying technologies and the researchers also underlined that learners’ technology
preferences are not limited to a particular tool. It is obvious from the literature that both learning preferences and
learning styles are at least having a degree of impact on some of the variables like motivation, success and interaction in
online learning environments.
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6. Possible E-Learning Styles
For determining effective e-learning styles that will be worth taking into consideration in online environments,
researchers asked four open-ended questions to distance learners. Voluntarily, 161 learners answered the open-ended
questions. The questions were:
(1) How do you think that you learn best?
(2) What are you doing for remediation?
(3) In what way do you prefer to study?
(4) What precautions do you take to be more successful while studying?
The researchers analyzed the qualitative data and found several emerging themes. These themes were used to reveal
and clarify the properties of e-learners. Hence, after reading through the considerable literature and filter what is
relevant to an online perspective, and based on the qualitative analysis of open-ended questions, and analyzing learning
styles proposed by Memletics Learning Style Inventory (2004), Kolb (1976), Felder and Soloman (1991), Felder-
Silverman Learning Model (1988), and Grasha-Reichmann Learning Style Scales (1996) in depth, researchers ended up
with eight dimensions of learning styles that should be considered in online environments. These eight e-learning styles
are classified as shown in Table-1. In fact, there are numerous characteristics of individuals, but for this study the main
goal was to detect the most general and common aspects that can be aligned with possible instructional methods, media
and materials for e-learning.
Table-1 E-Learning Styles
Individual/Solitary Learning Social/Collaborative Learning
An individual learner;
prefers to study on their own,
reserves a long time to think about the topics related
to her/his on life,
prefers to study independently with facilitation,
takes her/his own responsibility for learning,
trusts herself/himself for her/his ability to learn,
prefers to engage in asynchronous learning activities
(forum, blog, wiki etc.), and
engages in group activities after self-preparation
A social learner;
likes to engage in interactive group activities,
places importance on communication with instructors
and other learners,
prefers activities and projects that require group work,
thinks that learning is the common responsibility of
the instructor and learner,
likes to facilitate and help other learners,
enjoys to engage in synchronous learning activities
(chat, virtual classroom, whiteboard application etc.),
likes to contribute and manage group work.
Auditory Learning Visual Learning
An auditory learner;
thinks that she/he learns best by “hearing”,
likes listening to music while travelling, working and
loves to hear about the experiences of various
distinguishes between different sounds and
recognizes sound,
plays an instrument or sings songs,
dislikes silent places, and
prefers instructors who explain the topic in detail.
A visual learner;
thinks that she/he learns best by “seeing”,
likes mostly mathematics, science and technology,
easily finds her/his way by using maps,
prefers books that contain pictures, tables and comics,
easily remembers visual objects, plans and situations,
likes art, drawing and geometry, and
enjoys taking pictures and videos of the environment.
Concrete Learning Abstract Learning
A concrete learner;
thinks that she/he learns best by “doing”,
likes activities like sport and dance,
enjoys working with handcrafts like ceramics and
likes touching objects, clothes and furniture,
enjoys learning through simulations and playing
likes dealing with problems needs creativity, and
enjoys exploring and researching.
An abstract learner;
thinks that she/he learns best by “reading”,
links between what she/he heard and saw previously
in daily conversations,
enjoys telling stories and jokes,
prefers subjects like literature, history and foreign
prefers discussing problems and thoughts rather than
working on them,
has a wide range of vocabulary and likes to use the
right word in the right situation, and
expresses herself/himself orally or in writing very well
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Logical Learning Sensual Learning
A logical learner;
thinks that she/he learns best by “thinking in detail”,
likes activities requiring calculation,
likes playing logical games and solving puzzles,
prefers studying step by step with the guidance of a
dislikes making preferences during the learning
is extremely realistic, and
understands the whole if she/he understands the
A sensual learner;
thinks that she/he learns best by “relating with
prefers random processes rather than hierarchic ones,
uses emotions while solving problems,
likes being provided with various resources and
dislikes the planning of her/his own learning process
by others,
is too much creative, and
understands a combination of pieces if she/he
understands the whole.
These items can easily be converted into complete sentences and used as a learning style inventory which was
another goal of this research. Due to this reason, the number of items appearing under the headings is equal. Realizing
the different characteristics of our students in online environments and using this data to address all diverse learning
styles should increase the probability of knowing our students, providing them with suitable instructional media,
methods and environment, making the content easier for them to study and learn, thus increasing the success of the
process and products of online learning (Gülbahar, 2005b). So, as educators we should try to reveal the characteristics
of our students and reshape our courses based on the emerging characteristics of the class.
7. Conclusion
This chapter is written to reveal the possible relationship between e-learning and the use of learning styles as a means to
support and enhance learning. As stated by Palloff and Pratt (2003), “Although the Internet is cited as being a big
equalizer, dealing with learners should be done considering individual differences in learning, gender, culture, and
ability” (p.13). Rogers and McNeil (2009) similarly stated that the quality and efficacy of e-learning will be enhanced
by further investigation into the factors that impact student success. One of these factors that will be enhanced by
technology integration is learning styles. Gulbahar and Yildirim (2006) conducted research based on individual
differences and they concluded it was necessary to present learners with; (a) all possible types of media and material
sources, (b) course handouts in various formats, (c) a wide range pre and post activities in addition to the content, (d)
enough guidance with various add-ons and (e) a little humor, even for adult learners. They stated that this approach
increases the possibility of reaching learners who have diverse learning styles. Already mentioned by Graf, Kinshuk and
Liu (2009), Akdemir and Koszalka (2008), Maddux, Ewing-Taylor and Johnson (2002), and Thiele (2003), adequate
and appropriate support strategies should be provided to students with different learning styles and online course design
should be adopted to accommodate diverse styles when designing e-learning environments.
Cooze and Barbour (2007) underlined the importance of considering e-learning styles: “By utilizing the knowledge
gained through learning style inventories and descriptors, the e-teacher should have a greater repertoire of skills to
support learning in the virtual classroom and ultimately reach out through and beyond the tools in order to provide
quality instruction for all learners.” (p. 15).
In fact, online learning environments possess passive learning features when a student reads, listens, and analyzes
graphics, and consist of active learning features when students discuss and express themselves through writing in
various platforms. Hence, either grouped like the proposed one, or under different names, to address all possible
learning styles in terms of instructional media and materials, instructors should aim to at least provide (a) synchronous
and asynchronous learning activities, (b) individual and group work, and (c) supportive interaction and facilitation for
individual and social learners; (a) audio and visual materials, (b) podcasts or visuals of sample cases and scenarios, (c)
graphic organizers like diagrams, figures, comics and tables within the content, and (d) video casts of teaching
performances for auditory and visual learners; (a) hands-on activities, (b) interactive experiences like simulations and
games, (c) activities that needs discussion, creativity, exploration and research, and (d) wide range of printed materials
like books, hand-outs, worksheets, puzzles, and newspapers for concrete and abstract learners; and finally (a) content
considering both inductive and deductive approaches, (b) real-life problems, and (c) guided work plan for logical and
sensual learners. From the perspective of instructional methods; instructors should use a mix of all the appropriate
methods such as direct instruction, lecture, demonstration, discussion, cooperative learning, case studies, discovery
learning, problem-based learning, role-playing, scaffolding, and storytelling in harmony. All of these thoughts and some
additional thoughts from Soles and Moller (2001) are summarized in Table-2, just to give an idea to e-instructors.
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Table-2 Possible instructional media, materials and methods for addressing all e-Learning Styles
Learning styles Prefers Instructional Media and Materials Instructional Methods
Solitary Learning Self-study
Asynchronous learning activities that
need discussion, creativity, exploration
and research
Case studies, problem-based
learning and storytelling
Group work
Synchronous learning activities like
audio and videoconferencing, virtual
classroom, social media
Cooperative learning and role-
Auditory Learning Listening Audio materials like podcasts of sample
cases and scenarios, narrated tutorials
Direct instruction, lecture, case
Visual Learning Watching
Visual materials like videocasts, video
visuals of sample cases and scenarios,
simulations, graphic organizers
Demonstration and presentation
Concrete Learning Hands-on
Interactive experiences like simulations
and games, and activities that need
discussion, creativity, exploration and
Discovery learning and
problem-based learning
Abstract Learning Reading Printed materials like books, hand-outs,
worksheets, puzzles, and newspapers
Storytelling, discussion and
Logical Learning Thinking Real-life experiences (hierarchical),
linear instruction
Discussion, brainstorming and
critical analysis of real-world
Sensual Learning Feeling Creative experiences (random), rich
learning objects
Role-playing, case studies and
At this point, remembering the learning and facilitating principles for adults may be also useful. From the learning
styles point of view listed by MacKeracher (2009), (1) Adult learners have individual learning styles and mental
abilities and are heterogeneous in terms of these characteristics, (2) If learners’ and facilitators’ learning styles
mismatch, the result will be unsatisfactory, (3) Learning styles are value-neutral, a style adaptive in one situation may
not be adaptive in others, (4) Adults prefer to select learning situations and learning facilitating interactions
individually, and (5) Adults prefer to start from the learning activity they feel most comfortable with (p. 82-83). All of
these mean that, although you try to reach out learners’ characteristics and try to provide appropriate instructional
approaches, the preferences of learners may fluctuate for different content, instructor and so on. So, it is really
important to collect continuous data through an inventory, get feedback from the learners as to whether the results are
reasonable and appropriate, and re-organize all aspects to reach a more effective solution.
Since there are many researchers who found that learning styles are important for knowledge performance
(Manochehr, 2006) and should be considered for effective e-learning (Willems, 2007), it is e-instructors’ responsibility
to focus on students’ awareness of learning styles and provide a rich variety of instructional components to address all
learners. As also stated by Butler and Pinto-Zipp (2005-2006), “Educators must be challenged beyond the definitions
established in the pre-online era, identify the learning styles of online learners, and analyze the types of instructional
methods that are unique to online learning.” (p. 219). Because today the Internet has become a learning environment,
we should deal with developing quality learning for the online setting. As underlined by Cooze and Barbour (2007), our
main goal should be “…to consist of designing instruction which will foster and enhance learning for each student
regardless of their individual differences and irrespective of the setting for learning” (p. 16). Therefore, all models and
theories previously put forward should be reinvestigated in order to adapt to this new learning environment and
Acknowledgements The support by Asst. Prof. Dr. Cem Babadoğan is gratefully acknowledged for his excellent facilitation in the
process of reaching e-learning style types.
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... Over the past few years, digital media have improved the teaching and learning experiences and have become commonplace with university students and teachers (Alper & Gülbahar, 2004). In light of rising concerns about the spread of COVID-19, most higher education institutions have stop in-person classes. ...
... A survey was conducted and follow up interviews were done. A modified questionnaire adopted from Mercado (2008), Alper and Gülbahar (2004), and Toni Mohr et al. (2012) were used in the survey. This questionnaire included items about the respondents' demographic profile, level of online learning preparedness, attitude towards online learning, online learning styles, and instructional preferences. ...
... The online learning styles used in this study were those described by Alper and Gülbahar (2004), which is composed of eight dimensions -individual/solitary, social/ collaborative, auditory, visual, abstract, concrete, logical, and sensual learning. It is a learning style model often used in technology-enhanced learning previously designed for traditional learning (Graf et al., 2007). ...
... This if properly d esigned by considering learners' individual differences will provide sufficient learning resources and communication tools to build a collaborative learning environment where both students and instructors gain significant benefits. Gülbahar and Alper (2011) [5] stated that Learning preferences and learning styles are a way to enhance the quality of learning. They stressed that student can adapt learning processes, activities and techniques, if he/she is able to understand his/her own personal characteristics and the consequences of possible different experiences. ...
... This if properly d esigned by considering learners' individual differences will provide sufficient learning resources and communication tools to build a collaborative learning environment where both students and instructors gain significant benefits. Gülbahar and Alper (2011) [5] stated that Learning preferences and learning styles are a way to enhance the quality of learning. They stressed that student can adapt learning processes, activities and techniques, if he/she is able to understand his/her own personal characteristics and the consequences of possible different experiences. ...
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... A review on learning preferences of adult online learners has highlighted how individual learning styles and mode of instruction (online vs in-person) play a role in preferred methods of learning. 30 In our study, improvement in participant test scores was greatest in those who accessed the course material more often. However, we found that any exposure to the course resulted in improved scores. ...
Objective To determine the effectiveness of a 6-month, interactive, multi-modal, web-based EEG teaching program (EEG online ) in improving EEG analysis and interpretation skills for neurologists, neurology residents and technologists, particularly in resource-limited settings. Methods Between June 2017 and November 2018, 179 learners originating from 20 African countries, Europe and USA were registered on the EEG online course. Of these, 128 learners (91% African) participated in the study. Pre- and post-course multiple-choice-question (MCQ) test results and EEG online user logs were analyzed. Differences in pre- and post-test performance were correlated with quantified exposure to various EEG online learning modalities. Participants’ impressions of EEG online efficacy and usefulness were assessed through pre- and post-course satisfaction surveys. Results Ninety-one participants attempted both pre- and post-course tests (71% response rate). Mean scores improved from 46.7% ±17.6% to 64.1% ±18% respectively (p<0.001, Cohen’s d 0.974). The largest improvement was in correct identification of normal features (43.2% to 59.1%, p<0.001, Cohen’s d 0.664) and artifacts (43.3% to 61.6%, p<0.001, Cohen’s d 0.836 ). Improvement in knowledge was associated with improved subjective confidence in EEG analysis. Overall confidence among post-course survey respondents improved significantly from 35.9% to 81.9% (p<0.001). Lecture notes, self-assessment quizzes and discussion forums were the most utilised learning modalities. The majority of survey respondents (97.2%) concluded that EEG online was a useful learning tool and 93% recommended that similar courses should be included in EEG training curricula. Conclusions This study demonstrated that a multi-modal, online EEG teaching tool was effective in improving EEG analysis and interpretation skills and may be useful in resource-poor settings.
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This paper provides a novel approach to management education (online classes) design by using a combined approach of clustering (based on student engagement level in online mode) and conjoint analysis to design relevant online classes for different segments of management students. We base our study on the responses received from 280 business management students in a University in the eastern part of India. The findings bring those attributes to the light which are most important to different segments of management students based on the level of their engagement in online classes. This will help course instructors in management education to design better online classes.
... Research has shown that learning takes place through four major modes, namely concrete experience, reflective observation, abstract conceptualisation and active experimentation, with learners leaning towards some modes more than others (Gülbahar and Alper, 2011). Adult learners prefer achievement-oriented learning situations that utilise active approaches designed to integrate learning with their own experiences (Stevens, 2014). ...
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Universities are centres of frontier knowledge and skills, with the capacity to transform communities,when appropriately and adequately transmitted to users. The aim of this paper was to compare farmer-preferred learning methods and those utilised in teaching during Egerton University’s outreach activities, with a view to drawing useful insights for more effective community future engagements. A cross sectional survey was conducted in 2017, using researcher-administered questionnaires, on a sample of 84 farmers purposefully selected from communities where Egerton University implemented extension outreach programmes. Key informant interviews and focus group discussions were also conducted for community leaders and extension officers in the selected Wards, to supplement data collection. Results showed that the decision to participate in the outreach activities implemented by Egerton University was personal, with nearly all the respondents (99%) citing acquisition of new knowledge and skills as the major reason. Demonstrations were the most preferred and utilised methods (90 and 92%, respectively); while the use of group discussions were preferred by 51% of the respondents and utilised in 86% of the outreach activities. Results also showed significant relations for demonstration (c2 = 17.21, P<.001), touring university model farms (c2 = 68.11, P<.001) and use of training videos (c2 = 40.98, P<.001) between farmer-preferred learning methods and utilised teaching methods. This explains the popularity of demonstrations as a teaching and learning method of Egerton University in connecting theoretical and scientific aspects, to practice. Learner centred teaching methods, with the capacity to facilitate collaborative or cooperative learning, should be enhanced.
... In another definition, Duff (2000) described learning style as the composite of cognitive, affective and psychological characteristics that serve as an indicator of how an individual interacts with, and responds to, the learning environment. In other words, it can be described as a means of perceiving, processing, storing and recalling attempts in the learning process (Yasemin & Ayfer, 2011). It means that learning style is an indicator of how students perceive, interact and respond to learning environment. ...
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The main focus of educational assessment in the 21 st century is to promote learning. In this era of rapid development in Science and Technology, the teacher's role must change from that of transferring information and knowledge to that of advising, guiding and directing the learners to construct their own knowledge in a learning environment. In the Open and Distance Learning (ODL) environment where learners are made to learn how to learn on their own, the self learning materials and tools should be designed based on the learning styles of the learners. The assessment should also focus on the new realities. The purpose of this paper is to assess the learning styles of ODL students in Nigeria in order to design effective assessment processes that can promote their learning in the 21 st century. A cross-sectional survey was used in this study. 600 current ODL students from six study centres of National Open University of Nigeria (NOUN) were used as subjects. A questionnaire instrument was administered on the subjects for data collection. Results were presented on tables, analyzed using frequencies, percentages, means and aggregate means to answer research questions, while t-test of independent means was used to verify the hypotheses. The findings indicate that learners in NOUN posses different learning styles, and there is a significant difference in learning styles of students based on gender. It was recommended that the self study materials used by these students be designed based on the different learning styles.
... (Academic Resource Center, 2008). Through answering and understanding these questions educators can then be able to have insight into how they may fashion their instructional delivery to the learner's benefit.That instead of fixed learning styles strategies, adapting content to the learner, management educators should rather implement flexible learning strategies (Penger, 2009 The grouping of students according to a number of scales and questionnaires pertaining to the ways they receive and process information is defined as a learning style model (Gulbahar & Alper, 2014).The term Learning Styles is chiefly associated with Honey and Mumford who spent aconsiderable amount oftime on the topic and coming up with the Learning Styles questionnaire in 1982.They suggested that each of us has a predisposition to use a particular part of the learning cycle as our prime approach to learning. This gives four types of learners, activists, reflectors,pragmatist, and theorists (Honey & Mumford, 1982 ...
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Educating future health care practitioners is an important role for universities. Optimal learning environments consider how students learn and utilize various teaching methods to tailor curriculum delivery to match specified student learning preferences. Individualshave a preferential focus on different types of information, the different ways of perceiving the information, and the understanding of information. The grouping of students according to a number of scales and questionnaires pertaining to the ways they receive and process information is defined as a learning style model. Learning Styles is chiefly associated with Honey and Mumford who spent a considerable amount of time on the topic and coming up with the Learning Styles questionnaire in 1982. The study was a descriptive cross-sectional design. Purposive sampling was used. The study site was in the Kenya Medical Training College-Nairobi campus during the 2015/2016 year of study. A questionnaire composed of two questionnaires by Honey and Mumford (1982) and Neil Fleming (1987) was used. Statistical Program for Social Scientists (SPSS) v.25 was used to analyze the data collected. 124 responses were acquired majority being male 58.06%. First years were the majority 37.1%. Reflectors were the bulk of the population 66.1% and pragmatists the least 7.3%. First years were majorly 73.9% Reflectors with high significance relationship between the year of study and the learning philosophy (ꭓ 2 = 4.987, df=6 P=0.002). Reflectors stood out as the majority in all the learning styles, 47% of the respondents were Reflectors and Kinesthetic learners. On VARK majority of the students applied Kinesthetic as moe of learning. A significant association between the year of study and the learning philosophies (ꭓ 2 =6.56, p<0.0001 df=6). Similarly, there was a significant association between the gender of the participants and the learning styles (ꭓ 2 =3.56, p<0.001 df=6). It can be recommended that the learning preferences of physiotherapy students should be verified prior to the start of their academic tasks by using the VARK questionnaire and the categorization of learning into the philosophical classes. The preferred learning styles of medical students in the present study were aural and reading/writing styles. I would like to extend my gratitude to the students who participated in this study and completed the questionnaires.
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This research was designed to identify the learning styles preferences, and to explore the challenges, and learning strategies of the students of Virtual University of Pakistan. The research approach of the study was mixed method, in which quantitative approach was used for identifying the learning styles preferences of students of Virtual University in Pakistan. The qualitative approach was employed to find out the challenges faced by the students having different learning styles in virtual learning environment, and to identify the learning strategies which they used before and after starting online mode of education in order to cope with the academic requirements of virtual education and to improve their learning. In the quantitative phase of the study, a sample of 149 students of business administration in Virtual University of Rawalpindi, Islamabad, Gujarkhan, Kahuta and Muzafrabad city was identified according to the three main learning styles, i.e., auditory, visual and kinesthetic, and classified as auditory, visual and kinesthetic learners. A small group of 5 students (total 15) from each learning group was conveniently selected as the informants for the qualitative part of the study. Semi-structured, open-ended interviews were conducted with these participant students. There were considerable differences and similarities found in the challenges faced and learning strategies used by the students with respect to the virtual education.
In today's scenario, e-learning has become a significant part of the academic environment as well as of the corporate training sectors. Advancement in Information and Communication Technologies (ICTS) has brought new intersection of education, teaching, and learning that defines e-learning. E-learning systems deliver information for education at any time and at any place in an efficient manner. E-learning system consists of course content or learning materials in the form of nodes. These nodes are linked such that users can traverse the other nodes in the hypermedia environment. These learning concepts are available synchronously and asynchronously in different ways of representation. This presents learning materials in a disorganized manner to the learners. Due to this, learners may decline to adapt the learning material or may deviate from their goals. This requires a user model to respond to different needs of a learner. To handle the uncertainty of learner's mind while learning the concepts an intuitionistic fuzzy approach is used.
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The present study investigated (1) the impact of cognitive styles on learner performance in well-structured and ill-structured learning, and (2) scaffolding as a cognitive tool to improve learners' cognitive abilities, especially field dependent (FD) learners' ability to thrive in an ill-structured learning environment. Two experiments were conducted with 116 college students recruited from a large research I university in the west of the United States. Experiment 1 (n = 42) employed the group learning strategy to match learners' cognitive styles in asynchronous online learning. The results showed that the style matching strategy failed to yield expected gains in ill-structured asynchronous learning for FD learners. Different from the style-matching strategy, experiment 2 (n = 74) used a scaffolding model proposed by Cazden (1988) to improve FD learners' cognitive abilities in asynchronous online learning. Results indicated that focusing on learners' cognitive abilities proved to be more effective than style-matching strategy for FD learners in both ill-structured and well-structured asynchronous online learning. Implications of the findings were discussed with suggestions for future research.
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Personalized adaptive systems rely heavily on the learning style and the learner's behavior. Due to traditional teaching methods and high learner/teacher ratios, a teacher faces great obstacles in the classroom. In these methods, teachers deliver the content and learners just receive it. Moreover, teachers can't cope with the individual differences among learners. This weakness may be attributed to various reasons such as the high number of learners accommodated in each classroom and the low teaching skills of the teacher himself/herself, Therefore, identifying learning styles is a critical step in understanding how to improve the learning process. This paper presented an automatic tool for identifying learning styles based on the Felder-Silverman learning style model in a learning environment using a social book marking website such as . The proposed tool used the learners' behaviour while they are browsing / exploring their favorite web pages in order to gather hints about their learning styles. Then the learning styles were calculated based on the gathered indications from the learners' database. The results showed that the proposed tool recognition accuracy was 72% when we applied it on 25 learners with low number of links per learner. Recognition accuracy increased to 86.66% when we applied it on 15 learners with high number of links per learner.
This study investigated the influences of learning styles/preferences, prior computer skills and experience with online courses on adult learners' knowledge acquisition in a web-based special education course. Forty-six adult learners who enrolled in a web-based special education course participated in the study. The results of the study showed that (a) learning styles/preferences had significant effects on adult students' knowledge acquisition, and (b) there is a moderate positive correlation between computer skills and students' success. Data analysis also showed that there is no relationship between prior experiences with online courses and success in a web-based course.
The purpose of this paper is to report the results of a comparative and descriptive study that examined the relationship and effects of incorporating students’ learning styles in the design of instruction and the outcome of students’ learning, including their attitude and satisfaction. The paper will first explain how the literature on learning styles was used to develop a list of assumptions about learning styles, and further how these assumptions were used to identify a learning style model. It will also provide a detailed description of the process of using the learning style model to design and develop multiple instructional materials for two units of instruction for an online course. Finally, the paper will report the effects of this approach on students’ learning and their perception, attitude and satisfaction in comparison with instructional materials that are designed and developed on the basis of the content and objectives, without incorporating students’ different learning styles.