A Survey on Various Learning Styles Used in E-Learning System

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A Survey on Various Learning Styles Used in E-Learning System
Mr. T. Gopalakrishnan1
, Ms. V. S. Gowthami2
, Ms. M. Kavya3
Assistant Professor1
, PG Scholar2,3
Department of IT1,2,3
Bannari Amman Institute of Technology1,2,3
Abstract— Personalized E-learning seeks to provide each individual learner with the right and
sufficient content they need according to learners level of knowledge, behavior and profile. The
performance of the learners can be enhanced by posting the suitable E-learning contents to the learners
based on their learning styles. Hence, it is very essential to have a clear knowledge about various
learning styles in order to predict the learning styles of different learners in E-learning environments.
However, predicting the learning styles needs complete knowledge about the learners past and present
characteristics. The core objective of this survey paper is to outline the working of the existing learning
style models and the metrics used to evaluate them. Based on the existing models, this paper identifies
Felder–Silverman learning style model as the appropriate model for E-learning so that it can enrich the
performance of the E-learning system.
Keywords— E-learning, E-learning contents, E-learning environment, Learning styles, Learners
I. INTRODUCTION
E-Learning adopts modern educational technology to implement an appropriate learning
environment. E-learning had gained lots of attention since it greatly reduces the drawbacks of the
traditional learning educational setting environment (Chen et al. 2004). The effectiveness in the design
of an E-learning system is based on the common rules and features of the learners to be engaged in the
learning process. Therefore, the success of E-learning environments is greatly influenced by the factors
like learning objects, content delivery, relevant information retrieval, performance evaluation and the
maintenance of the psychological level through identification of the individual learning styles of the
learners (Vranic et al. 2007). This paper depicts the different learning styles which are available in the
literature and provides a comparative analysis among them. In addition, it suggests the use of Fuzzy
logic for handling uncertainty in Felder–Silverman learning style model.
A learning style is “a particular way in which an individual learns” (Butler 1986). Numerous
measures and instruments including questionnaires, interviews and profile information have been used
in the past to identify the learning styles of the learners efficiently. These metrics are labeled as explicit
information provided to describe the characteristics of the learners during the assessment procedure
(Claxton and Murrell 1987). Therefore, the main objective of this paper is to analyze the related works
for identifying the individual learning style based on the learners’ behavior. This will be helpful to
deliver a suitable E-content to the learners based on their learning styles in an E-learning environment.
The psychological level of the learners in an E-learning environment is greatly attributed by the learning
styles of the learners involved in learning. However, in most of the existing E-learning frameworks, the
psychological level between the learners and E-learning contents is not well balanced. This is due to the
fact that the learning styles of the individuals vary from one person to another and hence if the same
kind of E-contents is provided to all the learners, the success of the E-learning system degrades.

International Journal of Modern Trends in Engineering and Research (IJMTER)
Volume 01, Issue 05, [November - 2014] e-ISSN: 2349-9745, p-ISSN: 2393-8161
Therefore, the contents developed for the E-learning system could be modified based on the learners
learning styles in such a way that all the learners could be well benefited so that the objective of the E-
learning system could be satisfied. Hence, a deep understanding of the learners is necessary in predicting
their learning style in E learning environment
II. PAST LEARNING STYLE MODELS AND INSTRUMENTS
Learning styles are different kinds of methods and characteristics used in learning. The major
objective of identifying the learning style is, to well educate the performance level of the learners and
aiding them to find their best position to fit in the outside environments. Especially, in an E-learning
environment, the impact of the learning style causes a greater effect on the performance of the learners
and in the design of the E-learning systems. Several learning style assessment models and instruments
are available online to effectively assess the learning style (Hawk and Shah 2007; Brokaw and Merz
2000; Coffield et al. 2004). The generic categorization of the learning styles fall under four categories
namely Synthesis Analysis, Methodical Study, Fact Retention and Elaborate Processing. Among these,
the Synthesis Analysis deals with the processing of information and organizing them into taxonomy.
Methodical Study deals with careful study and completion of academic assignments. Fact Retention is
the analysis of the correct output instead of understanding the logic behind it. Elaborate Processing is the
application of new ideas to the existing knowledge. The categorization of learning styles is helpful to
group the learners and to provide relevant assistance in learning.
Figure 1. Various Learning Style model
2.1 Kolb Learning Style Indicator
The learning style proposed by David Kolb (Boyatzis and Kolb 1997) is an indicator based on
“Experiential Learning Theory” which considers experience of a learner as an important factor in
learning. Therefore, it discussed two kinds of experiences namely grasping and transforming
experiences. Grasping consists of two sub categories namely concrete experience and abstract
conceptualization. Similarly, the transforming experience has two sub categories termed reflective

observation and active experimentation. These experiences have been defined as follows Cornwell and
Manfredo (1994).
1) Concrete Experience: A new experience of situation is encountered, or a reinterpretation of
existing experience.
2) Reflective Observation: A new experience. Of particular importance are any inconsistencies
between experience and understanding.
3) Abstract Conceptualization: Reflection gives rise to a new idea, or a modification of an existing
abstract concept.
4) Active Experimentation: The learner applies them to the world around them to see what results.
From these categories, it can be observed that the first three experiences shown in this model
namely sensing, planning and watching others must be strengthened and the fourth one must be
improved. Moreover, David Kolb developed a Learning Style Inventory (LSI) in the year 1971, to assess
the individual Learning Styles based on Experiential Learning Theory. Using LSI the individuals were
tested and four types of learners were recognized. They are
1) Diverging: Its main learning abilities are Concrete Experience (CE) and Reflective Observation
(RO).
2) Assimilating: Its main learning abilities are Abstract Conceptualization (AC) and Reflective
Observation (RO).
3) Converging: Its main learning abilities are Abstract Conceptualization (AC) and Active
Experimentation (AE).
4) Accommodating: Its main learning abilities are Concrete.
5) Experience (CE) and Active Experimentation (AE): This learning style is important for
effectiveness in action-oriented careers such as marketing or sales.
This categorization helps to develop learning materials to the learners based on their experience
and learning styles.
2.2 Honey and Mumford’s Learning Styles Questionnaire
This model deals about the general behavioral tendencies. The Learning Style questionnaire of
this model is derived from David Kolb’s LSI. This model probes the learners to indicate their general
behavior tendencies rather than directly asking their behavior through questionnaires. Their reasoning is
most people have never consciously considered how they really learn. According to this model, learners
are classified into following four types.
1) Reflectors: Prefers to learn from activities that allow them to watch, think, and review (time to
think things over) what has happened.
2) Theorists: Prefers to think problems through in a step-by-step manner like lectures, analogies,
systems, case studies, models and readings.
3) Pragmatist: Prefers to apply new learning to actual practice to monitor if they work.
4) Activist: Prefers the challenges of new experiences, involvement with others, assimilation and
role-playing.
The learners’ general behaviors are examined in this model whereas they are attained from the
questionnaires in the Kolb’s model.
2.3 Gregorc Style Delineator
This model is based on cognitive thinking aspects of an individual rather than experiences
considered in Kolb model and general behavior tendencies discussed in Honey and Mumford model. In

this cognitive model, the existence of perceptions leads to the notions of different kinds of learning
styles. Therefore, this model describes two kinds of perceptual qualities namely Concrete and Abstract
and two kinds of ordering abilities namely Random and Sequential. In addition, the individuals also
possess different kinds of perceptual and ordering abilities like
1) Concrete Sequential: Learning with hands-on experiences.
2) Abstract Random: Receive instruction in an unstructured manner.
3) Abstract Sequential: Use logic to grasp situations.
4) Concrete Random: Prefers trial and error approach.
2.4 Fleming VAK Model
The VAK learning styles model suggests that most people can be divided into one of three
preferred styles of learning namely Visual, Auditory and Kinaesthetic.
1) Visual learning style involves the use of seen or observed things, including pictures, diagrams,
demonstrations, displays, handouts, films, flip-chart, etc.
2) Auditory learning style involves the transfer of information through listening: to the spoken
word, of self or others, of sounds and noises.
3) Kinesthetic learning involves physical experience - touching, feeling, holding, doing, and
practical hands-on experiences.
These categories are helpful in developing efficient machine learning techniques for effective E-
learning
2.5 Dunn and Dunn Learning Style
This model defines learning style as the way in which individual learners begin to concentrate
on, process, and retain new and difficult material. It is a combination of many biological and
experiential characteristics that work on their own or together as a unit to contribute to learning.
According to this model, the following learning style preferences need to be considered when instruction
is created and implemented:
1) Environmental: Noise level, lighting, temperature, and furniture/seating design.
2) Emotionality: Motivation, responsibility, persistence, and need for structure.
3) Sociological: Learning groups, presence of authority figures, varied working patterns, and adult
motivation (LSI only).
4) Physiological: Perceptual strengths, time-of-day energy levels, intake, and mobility.
5) Processing inclinations: Right or left, global or analytic and impulsive or reflective (Dunn and
Dunn 1989).
2.6 Chris Jackson Model
This model incorporates a new type of learners called Deep Learning Achiever which takes
motivation from the experimental model of learning (e.g. Kolb 1984). The hybrid model considers four
types of personalities.
1) Sensation Seeker: The learners with measuring exploration and curiosity.
2) Goal Oriented Achiever: This type of learner has mastery on long term and hard outcomes
3) Conscientious Achiever: It deals with perseverance and responsibility.
4) Emotionally Intelligent Achiever: The learners who provide rational and logical thinking come
under this category.

5) Deep Learning Achiever: This type of learners provides well thought out and well-constructed
outcomes.
This psychological modeling helps to understand the learners’ behavior which in turn can be
used to develop a suitable intelligent agent for providing tutoring in E-learning.
2.7 Carl Jung and Myers Briggs Type Indicator
Myers–Briggs Type Indicator (MBTI) is initially established by Carl Jung. The learning style
assessment in this kind of indicator is resolved using different aspects inclusive of psychological,
decision-making, information gathering and actions (Brokaw and Merz 2000). The models of learning
styles are as follows
1) Judging vs Perceiving: Attention towards the external world/things or internal world/things.
2) Thinking vs Feeling: Perceive world directly or perceive through impressions/imaging
possibilities.
3) Sensing vs Intuition: Learners taking decisions through logic or through mere human values.
4) Extroversion vs Introversion: Learner looking the world as a structured, planned environment or
as a spontaneous environment.
The major application of this indicator was using among the companies in order to enhance the
inter-personal relations among the employees by obtaining their psychological behavior.
2.8 Howard Gardeners Multiple Intelligence
This is based on multiple intelligences and was developed during 1983 at Harvard University.
The intelligence of an individual was tested using I.Q. testing, which is far limited. A broader range of
human potential was found in eight different notions of intelligence (Gardner and Moran 2006).
Gardener’s theory has emerged from recent cognitive research and hence a learner can learn, remember
and perform well in different ways (Gardner 2004). Different kinds of intelligences were observed as
different styles of learning which are as follows
1) Linguistic intelligence: This category of learners has extremely developed auditory skills and
often thinks in terms of words. It can be taught effectively by motivating them to read lots of
articles, books and papers.
2) Logical–Mathematical intelligence: This characteristic involves high interest in reasoning and
calculating and can be taught through logic games, investigations, unrevealing mysteries and
problem solving skill.
3) Visual–Spatial intelligence: This category of learners is well aware of the environments. It can
be taught by drawings, verbal and physical imagery.
4) Bodily-Kinesthetic intelligence: This type of intelligent learners is keen about body awareness
and can be taught through physical activities, hands-on experiences and role playing.
5) Musical intelligence: This category of learners is highly sensitive to music and rhythm and can
be taught by turning lessons into lyrics and speaking rhythmically.
6) Interpersonal intelligence: This deals with learners who have interaction with others and can be
taught by conducting group discussions.
7) Intrapersonal intelligence: The learners who shy away from others and do not interact with others
fall under this group and can be taught by independent study and introspection.
8) Naturalist intelligence: This type of learners relates their understanding to one’s natural
surroundings and can be taught by asking them to apply their knowledge to environmental
related applications like farming and gardening.

The theory of multiple intelligences provides a broader thinking approach to different styles of
learning as compared to traditional levels of learning styles (Gardner 2004). Gardener challenges that
everyone can learn the same material and show their efficiency when they are taught according to their
interest as indicated above. This kind of teaching will effectively suit the broad spectrum of students
with varying capabilities and interests
2.9 Felder–Silverman Index of Learning Styles
This learning style model (Felder and Silverman 1988) often used in technology enhanced
learning and designed for traditional learning. Additionally, Felder–Silverman learning style model
defines the learning style of a learner in more detail and distinguishing between preferences on four
dimensions. This model provides four dimensions of learning based on psychological aspects of the
learners which is found to be important in an E-learning environment. The learning styles proposed by
Felder–Silverman for categorizing the learners are:
1) Active/Reflective: Active learners learn by doing something with information. Reflective
learners learn by thinking about information.
2) Sensing/Intuitive: Sensing learners favor to take in info that is concrete and practical. Intuitive
learners favor to take in info that is original, abstract and oriented towards theory.
3) Visual/Verbal: Visual learners prefer visual presentations of material - pictures, diagrams, flow-
chart and graphs. Verbal learners prefer explanations with words – includes both written and
spoken.
4) Sequential/Global: Sequential learners prefer to organize information in a linear, orderly fashion.
Global learners prefer to organize information more holistically and in a seemingly random
manner without seeing connections.
Since most learners fall in the category of either active or reflective for the first dimension, this
model is more suitable to evaluate the learners in an E-learning environment.
Table 1. Comparison of Various learning style model
S.
No
Learning Style
Model
Year Learning Theory
Model
Learning Style
Preferences
Limitations
1 David Kolb model 1983 Experiential learning
theory
Diverging Mixed empirical results and
low to motivate predictive
reliability
Assimilating
Converging
Accommodating
2 Honey & Mumford
Model
1985 Behavioral theory Reflectors Assumed to acquire
preferences that are adaptive,
either at will or changing
circumstances
Theorists
Pragmatists
Activist
3 Gregorc model 1982 Cognitive theory Concrete sequential Some qualities and ordering
abilities are more dominant
within certain individuals
Abstract random
Abstract sequential
Concrete Random
4 Flemming VAK
Model
1992 Meta-learning theory Visual Low validity and reliability
Auditory
Kinaesthetic

5 Dunn and Dunn 1989 Biological &
Experimental theory
Environmental Criticized for not considering
the differences among the
individuals
Emotionality
Sociological
Physiological
Processing inclinations
6 Chris Jackson/2002 2002 Neuro-psychological
theory
Sensation Seeker Contextual differences in the
dependent variableGoal oriented achiever
Conscientious achiever
Emotionally intelligent
Achiever
Deep learning achiever
7 Carl Jung and Myers
Briggs type indicator
1988 Personality theory Judging vs perceiving Lacks convincing validity
dataThinking vs feeling
Sensing vs intuition
Extroversion vs
introversion
8 Howard Gardeners
multiple intelligence
1983 Intelligence theory Linguistic intelligence Detecting additional
intelligences is not easy and
is not well suited for all types
of individuals
Logical–mathematical
intelligence
Visual–spatial
intelligence
Bodily-kinesthetic
intelligence
Musical intelligence
Interpersonal intelligence
Intrapersonal intelligence
Naturalist intelligence
9 Felder–Silverman
Index of learning
styles
1988 Psychological theory Active–reflective Dependencies between two
styles exist and hidden
dimensions present in dataset
produces a greater impact on
identification
Sensing–intuitive
Visual–verbal
Sequential–global
III. CATEGORIZATION OFEDUCATIONALSYSTEMS
The use of internet in education has provided a new revolution known as E-learning or web
based learning (Myller et al. 2002). In the traditional offline learning system, instructors and the learners
were directly involved in the teaching-learning process (Vranic et al. 2007). In such a scenario, they
used the course materials and discussed them through teaching and dialogues. On the other hand, in the
current internet world, E-learning is another important area that is to be strengthened since most of the
learners prefer to read web contents and web-based course materials for learning (Myller et al. 2002).
Subsequent to E-learning, the learning activities are enhanced by providing intelligent tutoring systems.
In a web-based learning system, the learners understanding is measured based on the reproduction of the
material studied by them. Artificial Intelligence techniques such as page ranking, rules, agents and
machine learning algorithms are used to classify and improve the performance of the learners by
providing suitable E-learning documents (Chen et al. 2004).
The iLessons system developed by Bergasa-Suso et al. (2005) overcame the limitations of the
previous E-learning systems by providing two additional features namely Drag and Drop facility for
reuse and an intelligent recommendation system that recommends relevant web pages to the learners

based on their learning styles. Therefore, iLessons is an important contribution for E-learning through
the identification of learners as active or reflective. The work done by Bergasa-Suso et al. (2005)
promptly followed an algorithm in identifying the first dimension and classified the users into two kinds
as either Active or Reflective. From his works it is understood that the learners belonged to either of the
dimensions crisply. It is evident from their experiments that the classification accuracy for identifying
such learners was 71% when considering the first dimension of Felder–Silverman learning style model.
Sanders and Bergasa-Suso (2010) proposed an intelligent E-learning system with an effective
user interface. The main advantage of this intelligent E-learning system is that it is capable of
performing deductive inference to obtain the learning style of learners. They followed Felder–Silverman
learning style model and developed an E-learning system that can infer the learning styles in real time
and provided recommendations for selecting suitable E-learning contents. This is a significant
achievement in the area of E-learning since; it uses Artificial Intelligent techniques for classifying the
learners based on their learning style into three categories namely active or reflective or unknown. He
categorized in such a way, since the users may not fall into one category virtually all the time and most
learners tend towards a particular dimension in course of time, environmental factor, mood, need and
psychological changes. The accuracy of classifying the learners Sander’s et al has increased to 81%
compared to the earlier work done by Bergasa. One of the important contributions of Sanders et al. is the
inclusion of unknown sets in addition to the active and reflective categories of learners.
Juan Yang et al. (2014) proposed a new dynamic learning style prediction method based on a
pattern recognition technique. This method functions as a form of middleware that can be applied to
other intelligent tutoring systems, while it can process topic-dependent data to make predictions and
update learning style profiles in a recursive manner. This prediction mechanism is middleware but it still
needs a benchmark to indicate its functionality and effectiveness. In this, they introduce the benchmark
system, which is a prototype system called “Programming in Java” (PIJ). The main function of PIJ is to
collect data on learning behaviors using log files. To improve the computational efficiency and to
decrease the complexity, the organization of learning objects (LOs) in PIJ follows three distinct rules.
1) The overall content comprises a series of topics in a given order, which is consistent with their
prior/ subsequent constraints.
2) Each topic must contain a sufficient number of LOs to provide the various types of LOs
demanded by learners.
3) The construction of the LOs within a single topic follows the star topology.
The learning style used is that proposed by Felder & Silverman for engineering students, which
distinguishes between learning preferences based on the following five dimensions.
• Sensing/Intuitive: The way learners perceive information
• Visual/Verbal: The way learners acquire information
• Inductive/Deductive: The way learners organize information
• Active/Reflective: The way learners process information
• Sequential/Global: The way learners understand information
The main advantage of this approach is that it can allow several different learning style families
to be used together because their mutual similarity-based learning style preference patterns are
independent of each other. Suppose that there are t dimensions in the learning styles of the Felder &
Silverman model, the target function f(X) used for pattern recognition is set as

Min f(X),
f(X) = var(M, )
=
Where X is a vector X = ( ), and t is the dimensionality of the learning styles. The above
formula is used to identify the key learning style similarity matrix that represents the current collective
learning behaviors. After identifying the key learning style dimension, where the learning information of
the subject has the highest projected value in the multidimensional space, the learning style dimension k
should be used as a scale tool to cluster the new learners according to their learning behaviors. This
clustering process is simple compared with the clustering processes used in previous studies because the
multi label classification problem has been transformed into a single label classification problem. The
learning information pattern evolves dynamically in different learning contents. Thus, learning
information pattern recognition should be processed recursively for different learning topics to obtain a
more accurate learning style prediction result.
They used the ILS to survey 50 sample students who were majoring in computer science. These
were second year students from three different schools: Sichuan Normal University, Southwest
University, and Chongqing University of Posts and Telecommunications. The students helped us to
construct their learning style preference patterns and learning information patterns. Thus, the average
accuracy of the predictions in the present study was about 75 percent which was relatively high
compared with previous methods.
IV. CONCLUSION
In this paper, a survey of on different learning styles which were identified by various
researchers with suitable discussions on them has been provided. For this purpose, both the traditional
offline learning and E-learning works have been considered and their methods are analyzed for
highlighting their salient features. From this analysis, it has been found that Felder– Silverman style is
more suitable learning method for adopting in E-learning. In addition, an intelligent E-learning system
that can handle uncertainty effectively has been proposed and compared with the related work. The
survey of learning methods provided in this paper helps to develop effective course wares and intelligent
tutoring systems. Further works in this direction can be the provision of a survey on the use of machine
learning techniques which will be helpful for enhancing the learning materials provided in web based
learning.
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