Creative Education
2013. Vol.4, No.10, 627-632
Published Online October 2013 in SciRes (
Copyright © 2013 SciRes. 627
The Effect of Students’ Learning Styles to
Their Academic Success
Murat Gokalp
Faculty of Education, 19 May University, Samsun, Turkey
Received August 6th, 2013; revised September 6th, 2013; accepted September 13th, 2013
Copyright © 2013 Murat Gokalp. This is an open access article distributed under the Creative Commons Attri-
bution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
This study was aimed to evaluate the learning styles of education faculty students and to determine the
effect of their success and relationship between their learning styles and academic success. The popula-
tion of this study is comprised of the students of Education Faculty in 19 May University and the sample
includes 140: 68 art, 72 pre-school teacher department students. Depending on the results obtained from
pre-test, it was aimed to improve students’ knowledge and skills in studying. There was a significant dif-
ference between the scores of pre- and post-tests. The significant relationship between the scores of
post-test and the student success revealed that they learned how to study effectively. The validity and re-
liability of the test were determined by considering the Cronbach alpha coefficients for each and all of the
items. The study has found statistically significant differences between the results of the first and final
applications of the subtests on learning styles and academic success; those subtests covered the items as
learning, planned study, effective reading, listening, writing, note taking, using the library, getting pre-
pared for and taking exams, class participation and motivation.
Keywords: Study Skills; Learning; Education; Success; Learning Styles
It is commonly believed that learning styles are not really
concerned with “what” learners learn, but rather “how” they
prefer to learn and it is also an important factor for students’
academic achievement and attitudes. Students have different
strengths and preferences in the ways how they take in and
process information which is to say, they have different learn-
ing styles. Some prefer to work with concrete information (ex-
perimental data, facts) while others are more comfortable with
abstractions (symbolic information, theories Mathematical
models). It is common to describe and classify unique styles in
many domains. For example, there are various architectural
styles that may be classified by elements of form, material, time
period, and indigenous geographic region. Similarly, there are
many distinct literary styles, classified by form, genre, and
technique. However, style is not a term that is particularly
well-associated with the processes that comprise the complex
mechanism of individual learning.
However, recent research suggests that the style by which
one learns and applies knowledge is an important characteristic
to consider in the aggregate educational processes (Graf, Lin, &
Kinshuk, 2008; Kolb & Kolb, 2009; Syler et al., 2006; Thor-
ton, Haskell & Libby, 2006; Zualkernan, Allert, & Qadah,
2006). Acknowledgement of unique learning styles is an at-
tempt to characterize the complex processes by which one ac-
quires knowledge (Kolb, Rubin, & McIntyre, 1974). Learning
style may be thought of as a formulation of preconceptions by
an individual engaged in the activity of learning (Biggs &
Moore, 1993). The Dual Coding Theory for example states that
information is processed through one of two usually independ-
ent channels (Beacham et al., 2002).
A learning style is defined as the characteristics, strengths
and preferences in the way how people receive and process
information (Felder & Silverman, 1988; Allinson & Hayes,
1996; Felder & Brent, 2005; Hsieh et al., 2011). It refers to the
fact that every person has his or her own method or set of
strategies when learning (Schemeck 1988; ChanLin, 2009; Ford
& Chen, 2000; Weinstein, 1996). Learning styles are not di-
chotomous (black or white, present or absent). Learning styles
generally operate on a continuum or on multiple, intersecting
continua (Ehrman, 1996; Dunn, 1983; Reid, 1995; McDermott
& Beitman, 1984).
There are many debates within the higher education commu-
nity on how teaching or teaching effectiveness may be defined,
for instance, defining effective teaching as “that which produces
beneficial and purposeful student learning through the use of
appropriate procedures including both teaching and learning in
their definition”, and defining effective teaching as the “crea-
tion of situations in which appropriate learning occurs; shaping
those situations is what successful teachers have learned to do
effectively”. Learning styles are generally considered as char-
acteristic, cognitive, affective, and psychological behaviors that
serve as relatively stable indicators of how learners perceive,
interact with, and respond to a learning environment.
Even though there are various definitions of learning styles
which are unique and steady, methods of effective learning and
information processing are widely accepted (Butler, 1987; Can-
field & Canfield, 1988; Keefe, 1991; Weinstein, 1996). A good
strategic learner must understand how to identify their learning
goal, integrate the learning style, apply proper skills, and be
self-regulated to achieve the best results from learning (Paris &
Wingrad, 1990; Zimmerman & Schunk, 2001; Wadsworth,
Husman, & Duggan, 2007). Teaching methods also vary.
Teaching and learning are the two sides of a coin. The most
accepted criterion for measuring good teaching is the amount of
student learning that course. There are consistently high corre-
lations between students’ ratings of the “amount learned” in the
course and their overall ratings of the teacher and the course.
Those who learned more gave their teachers higher ratings.
Some instruct lecture, others demon strate or discuss; some
focus on principles and others on applications; some emphasize
memory and others on understanding. In literature there exist
numerous learning styles and learning style models. The dif-
ferences among definitions and models result from the fact that
learning is achieved at different dimensions and that theorists
define learning styles by focusing on different aspects (Shuell
1986; Dede, Brown-L’Bahy, Ketelhut, & Whitehouse, 2004;
Jensen, 1998). Explaining that “different ways used by indi-
viduals to process and organize information or to respond to
environmental stimuli refer to their learning styles”, defines
learning style as a sort of way of thinking, comprehending and
processing information. To Kolb (1984), learning style is a
method of personal choice to perceive and process information.
In this sense, learning style is, on one hand, sensory and, on the
other hand, mental. In the 1940s Isabel Briggs Myers developed
the Myers-Briggs Type Indicator (MBTI), an instrument that
measures, among other things, the degree to which an individ-
ual prefers sensing or intuition. In the succeeding decades the
MBTI has been given to hundreds of thousands of people and
the resulting profiles have been correlated with career prefer-
ences and aptitudes, management styles, learning styles, and
various behavioral tendencies (Myers, 1980; Kolb, 1984). The
complex mental processes by which perceived information is
converted into knowledge can be conveniently grouped into
two categories: active experimentation and reflective observa-
tion. Active experimentation Kolb showed that learning styles
could be seen on a continuum running from: 1) concrete ex-
perience: being involved in a new experience, 2) reflective
observation: watching others or developing observations about
own Experience, 3) abstract concept ualization: creating theo-
ries to explain observations, 4) active experimentation: using
theories to solve problems, and make decisions. Kolb’s learning
styles gave examples of how one might teach to them: 1) for the
concrete experiencer: offer labs, field work, observations or
videos, 2) for the reflective observer: use logs, journals or
brainstorming, 3) for the abstract conceptualizer: lectures, pa-
pers and analogies work well, 4) for the active experimenter:
offer simulations, case studies and homework. It involves doing
something in the external world with the information to discuss
it or explainit or test it in some way and reflective observation
involves examining and manipulating the information intro-
spectively. Induction is a reasoning progression that proceeds
from particulars (observations, measurements, and data) to
generalities (governing rules, laws, and theories). Deduction
proceeds in the opposite direction. In induction one infers prin-
ciples; in deduction one deduces consequences (Friedman &
Alley, 1984; Rose, 1998; Dervan & Lawrence, 1982).
Active experimentation involves doing something in the ex-
ternal world with the information to discuss it or explainit or
test it in some way and reflective observation involves examin-
ing and manipulating the information introspectively. The sim-
plest and most common form of which involves presenting the
information both textually and visually. “Whole brain” learning
is known to be a far more effective way to learn. The better
connected the two halves of the brain are, the greater the poten-
tial of the brain for learning and creativity is. Sequential learn-
ers follow linear reasoning processes when solving problems;
global learners make intuitive leaps and may be unable to ex-
plain how they came up with solutions. Sequential learners can
work with material when they understand it partially or superfi-
cially, while global learners may have great difficulty to do so.
Visual learners remember best what they see: pictures, dia-
grams, flow charts, time lines, films, and demonstrations. Ver-
bal learners get more out of words: written and spoken explana-
tions. Everyone learns more when information is presented both
visually and verbally. Visual learners most effectively process
visual information; auditory learners (Whitman & Schwenk,
1984; Miller, 2001) understand best through hearing; and kin-
esthetic/tactile learners learn through touch and movement. A
study conducted by Specific Diagnostic Studies found that 29
percent of all students in elementary and secondary schools are
visual learners, 34 percent learn through auditory means, and
37 percent learn best through kinesthetic/tactile modes.
Knowledge, attitudes and skills are the content areas needed to
produce a well-trained professional. In short, learning style
preferences of students cannot be the sole basis for designing
instruction, and prescription based on diagnosis must be tenta-
tive, varied, monitored, and verified. Project tasks that allow
students to use their individual learning styles are not a direct
path to higher-order thinking. However, it is possible to create
products that reflect shallow and superficial thought. In the
mid- to late 1970s, paradigms began to be developed to identify
the more external, applied modes of learning styles.
Style refers to a pervasive quality in the learning strategies or
the learning behavior of an individual, “a quality that persists
though the content may change” (Fischer & Fischer, 1979;
Ennis, 2000; Gregorc, 1979; Dale 1969; Diaz & Cartnal, 1999;
Smith & Renzulli 1984).
One of the components in the Dunn and Dunn model of
learning styles which probably has some biological basis is
time-of-day preference.
Indeed, recent research points to a genetic influence, or
“clock gene”, which is linked to peak alert time. Understanding
students’ learning styles has been identified as an important
element for e-learning development, delivery and instruction,
which can lead to improved student performance (Shih & Ga-
mon, 2002; Davidman, 1981; Archer et al., 2003).
A simple awareness of differences in student learning styles
is vital for educators in order to aid the learning process. Effec-
tive instruction reaches out to all students, not just those with
one particular learning style. Students taught entirely with
methods antithetical to their learning style may be made too
uncomfortable to learn effectively, but they should have at least
some exposure to those methods to develop a full range of
learning skills and strategies. Most people extract and retain
more information from visual presentations than from written
or spoken prose.
Generally, a rich data have been obtained through studies on
learning styles; however, the data have rarely been exploited by
designers of instructional programs thereby a greater under-
standing of learners’ approaches to learning can be obtained.
Copyright © 2013 SciRes.
All information which becomes the subjective life of an indi-
vidual after giving meaning process may have individual-spe-
cific differences in ensuring permanence of learning and re-
membering. To describe learning styles and to analyze which
factors affect learning styles, many studies have been con-
ducted for years. Learners have unique ways of learning, which
may greatly affect the learning process and consequently their
academic achievement and its outcomes. Learners learn in
many ways by seeing and hearing; reflecting and acting; rea-
soning logically and intuitively; memorizing and visualizing.
Researchers drew a distinction between learning styles and
strategies focusing on the ways they differ from each other. To
teach and learn more effectively, instructors and learners need
to better understand and appreciate these individual differences
and how they affect the learning process. Learning styles have
been extensively discussed in the educational psychology lit-
erature Students will learn content better through their preferred
learning style. We know that teachers tend to teach in their own
preferred learning style. Learning style includes how they ap-
proach learning, experience learning and utilize information.
Filling in questionnaires and quizzes to determine preferred
learning styles can be fun but will not be effective unless they
become part of an ongoing program of learning how to learn for
students. Learning styles refer to the variations in your ability
to accumulate as well as assimilate information. It is quite easy
to determine and you may have already had an idea that you
might have a particular learning style. In other cases, it may not
be quite easy to identify.
Data were collected by applying an evaluation test for study-
ing and learning activities Developed by the researcher, and
also by examining student grades. The test includes 106 ques-
tions about 10 sub topics covering Learning, Planned study,
Effective Reading, Listening, Class Participation, Writing, Us-
ing Library, Getting prepared for and Taking Exams, Motiva-
tion, Note Taking, The “t-test” was used in order to determine
whether there was a difference between test scores in prelimi-
nary and final applications of the items involved. A correlation
analysis was used to determine the relationship between pre and
post test scores in each item and also between these scores and
student success.
Participant and Settings: The population of this study is
comprised of the students of Ondokuz Mayis University Educa-
tion Faculty and the sample includes 140: 68 art, 72 pre-school
teacher department students. The study protocol was approved
by the school administration and the permission was obtained.
The students were informed about the purpose and content of
the study; they were told that their participation was voluntary
and their verbal consent was obtained. This study was aimed to
evaluate the learning styles of education faculty students and to
determine the effect of their success and relationship between
their learning styles and academic success. The validity and
reliability of the test was determined by considering the Cron-
bach Alpha coefficients for each and all of the items. SPSS 15
for windows was used for this purpose. This coefficient was
determined for each of the items and these coefficients are il-
lustrated in Table 1. Study is limited 140, 68 art, 72 pre-school
teacher department students at Ondokuz Mayis University
Education Faculty.
Table 1.
Cronbach values for each of the items.
Items (N = 106)
Learning 0.86
Planned study 0.84
Active reading 0.85
Listening 0.79
Class participation 0.84
Writing 0.89
Using the Library 0.78
Getting Prepared for and Taking the Exam 0.86
Motivation 0.71
Note taking 0.85
Research Results
The test was given to the students at the beginning and the
end of the academic year. Findings related to all items are
demonstrated in Table 2.
140 students who participated in the study had higher mean
scores in post-tests and the difference between pre and post test
mean scores was statistically significant (p > 0.05). A positive
relationship was observed between the scores of pre and post
tests on sub topics. The relationship between the pre and post-
test and grades of the students was examined by correlation
analysis. The findings are given in Table 3.
According to these results, a positive correlation was found
between the scores of post-test on the items of learning,
planned study, effective reading and grades while there was
weak negative correlation between the scores of pre-tests on the
items of learning, planned study, effective reading and grades at
the significant level of 0.05. While the correlation between
pre-tests scores in the items of listening and note taking and
grades wasn’t significant, the correlation between the scores of
post-test and grades was strongly positive. While there was a
weak negative correlation between the scores of pre-tests on the
items of class participation, writing, using library, getting pre-
pared for and taking an exam and grades (r = 0.007, r =
0.022, r = 0.018, r = 0.040 respectively), the relationship
between the scores of posttest and grades was reduced to a very
weak negative correlation (r = 0.300, r = 0.008, r = 0.034, r =
0.086 respectively).
Conclusion and Discussion
The study has found statistically significant differences be-
tween the results of the first and final applications of the sub-
tests on learning styles and academic success; those subtests
covered the items of learning, planned study, effective reading,
listening, writing, note taking, using the library, getting pre-
pared for and taking exams, class participation and motivation.
The students who did not have study plans or could not fol-
low their plans at the beginning of the term were observed to
have a well-planned study program at the end of the term.
In addition to the problem of the complexity of identifying
learning styles, Corbett and Smith (1984) discuss the problem
of the reliability of such learning style instruments as the Ed-
monds Learning Style Identification Exercise. Their study
Copyright © 2013 SciRes. 629
Table 2.
The difference between the pre and post-test.
Items (N = 106) X S t-test P r
Pre-test 26.80 4.60
Post-test 28.78 3.12
3.98 0.0000.60
Effective Reading
Pre-test 29.56 7.43
Post-test 40.90 6.59
2.79 0.0080.70
Pre-test 27.18 3.80
Post-test 29.10 4.25
4.97 0.0000.68
Class Participation
Pre-test 18.21 2.76
Post-test 20.37 3.11
7.87 0.0000.55
Using Library
Pre-test 48.26 6.90
Post-test 47.88 6.67
2.68 0.0150.70
Getting Prepared for
and Taking Exams
Pre-test 23.30 4.01
Post-test 24.90 3.23
5.96 0.0000.64
Pre-test 22.14 5.97
Post-test 24.05 5.98
3.68 0.0000.58
Note Taking
Pre-test 24.104 4.42
Post-test 25.54 4.70
4.22 0.0000.74
Pre-test 21.37 3.98
Post-test 22.48 3.54
3.70 0.0000.62
Planned Study
Pre-test 24.10 3.60
Post-test 23.47 3.88
4.307 0.0000.66
showed that individual variation tended to be consistent and
therefore suggestive of external reliability but that group varia-
tion lacked consistency and therefore tended to be less reliable
tolist three shortcomings of existing self-assessment instru-
ments: 1) The instruments are exclusive (i.e., they focus on
certain variables); 2) the students may not self-report accurately;
and 3) the students have adapted for so long that they may re-
port on adapted preferences. Finally, McLaughlin (1981),
Daniel, Price and Merrifield (2002) studied the effect of learn-
ing styles and learning environments on the distance education
of students in the department of physiotherapy. Werner (2003)
studies the effect of self-awareness about learning styles on the
selection of learning strategies and the development of com-
prehension process. Kolb Learning Styles Inventory was used
to identify the learning styles of forty-one adult learners who
were observed for six months. The subjects tackled strategies
Table 3.
Correlation between items and grades.
Items (N = 106) Grader
Effective Reading
Class Participation
Using Libr ary
Getting Prepared for and Taking Exams
Note Taking
Planned Study
and techniques on the basis of time, keeping the memory, read-
ing, note-taking and decision-making.
The data concerning the learning preferences of subjects
were collected through the compositions they wrote. The find-
ings of the study show that the learning types (strategies) pre-
ferred according to the learning styles of the subjects were not
the appropriate strategies. According to the findings of studies
conducted by using the Kolb Learning Style Inventory, learning
styles vary depending on individuals’ majors (social sciences,
natural sciences etc.) and occupations (Kolb, Boyatzis, &
Mainemelis, 2001). Kolb, Wolfe, Fry, Bushe and Gish (1981)
suggest that there are disciplinary differences in learning styles.
Programs should be designed to improve students’ learning
styles and learning strategies for all levels to make the teaching
and learning process more effective.
It is also recommended that course design should be flexible
enough to reach a variety of learning styles. One such example
is described by Bates and Leary (2001) which provides a four
tier delivery approach whereby the student progresses sequen-
tially through each level based upon their learning needs.
The students should be properly guided and given incentives
to select individual learning styles that are appropriate and ap-
plicable in their environment for them to achieve their personal
academic objective. The students should adopt a suitable learn-
ing style that would be beneficial to them.
Copyright © 2013 SciRes.
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