European Journal of Education Studies
ISSN: 2501 - 1111
ISSN-L: 2501 - 1111
Available on-line at: www.oapub.org/edu
Volume 3 │ Issue 4 │ 2017
doi: 10.5281/zenodo.344919
THE RELATIONSHIPS BETWEEN CRITICAL THINKING SKILLS
AND LEARNING STYLES OF GIFTED STUDENTS
Yalçın Dileklii
Education Faculty, Department of Educational Sciences,
Aksaray University, Aksaray, Turkey
Abstract:
The current study investigates the relationship between critical thinking skills and
learning styles of mentally gifted students. The participants were 225 gifted students in
Turkey attending Science and Art Centres which are after-school activity centers for
mentally gifted students. Participants were 9 -15 years old and were attending
secondary schools and high schools. The data were gathered using the Kolb Learning
Style Inventory and the Critical Thinking Scale and analyzed using Chi-Square, t test,
ANOVA and regression analyses. The findings revealed that gender was not a
significant variable for learning styles but it was a significant variable for critical
thinking skills. Gifted students had high scores on the Critical Thinking scale.
Relationships were also found between gifted students learning styles and their critical
thinking skills except in the analysis dimension of the Critical Thinking scale. Gifted
students who achieved the highest scores on the scale had assimilating and converging
learning styles.
Keywords: gifted students, critical thinking, critical thinking disposition, learning
styles, modes of learning
1. Introduction
The current educational era has witnessed curriculum revolutions in many countries
influenced by the constructivist movement. This revolution has also affected all
components of education including educational policy, teachers, and schools. The
revolution has launched new terms such as learning to learn,
Copyright © The Author(s). All Rights Reserved.
© 2015 2017 Open Access Publishing Group
teaching thinking
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skills , and creative thinking . Critical thinking is also one of these terms. This is
because, in today s world, an individual is exposed to many new and contradictory
ideas and challenging conditions during an ordinary day. From the time one wakes up,
one is exposed to many advertisements on TV, the internet, and other media, and in the
workplace one is again exposed to many new ideas and offers. Thus, an individual has
to decide on many things such as what to buy, where to go on holiday, and which news
reports to believe. To choose the best option, one should know how to compare the
options and to think deeply; in other words, one should know how to use critical
thinking (McKnight, 2000; Kenney, 2013). Despite many curricular reforms, teaching
critical thinking has not reached the desired level set out in curriculum goals. Many
researchers have tried to discover the reasons for this problem. As noted by Sternberg
(1987), many curriculums are doomed to fail to teach critical thinking because they are
prepared in advance of classes. Sternberg emphasizes teachers' and learners individual
differences. These differences include variations in students learning styles and IQ
levels (Demir, 2006; Güven & Kürüm, 2008; Kettler, 2014; Thomson, 2010). In the
literature, there are many studies analyzing the relation between critical thinking skills
and other variables such as age, gender, teaching and learning styles in general school
education. However, there are limited number of studies dealing with the relationship
between the learning styles and critical thinking skills of mentally gifted students. This
study attempts to answer the question of whether there is a relationship between the
critical thinking skills of gifted learners and their learning styles in Turkey.
2. Literature Review
2.1 Critical Thinking (CT)
In the literature, there are numerous definitions of critical thinking (e.g. Brookfield,
1987; Kurnaz, 2014, Lipman, 1988; Meyers, 1986). Robert Ennis (1985, p. 45) defined
critical thinking as reasonable and reflective thinking focused on deciding what to believe or to
do . Sternberg
, p.
defines the term as
the mental processes, strategies, and
representations people use to solve problems, make decisions, and learn new concepts .
Although there are many different definitions of the term, critical thinking and its
components were not identified until 1990 by a group of scientists who joined the Dephi
Panel managed by Facione in the USA (Facione, 1990). In the Dephi Report critical
thinking is defined as self-regulatory judgment which results in interpretation, analysis,
evaluation, and inference, as well as explanation of the evidential, conceptual, methodological,
criteriological, or contextual considerations upon which that judgment is based
Facione,
,
p. 4). For Facione, critical thinking has six components.
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The first is interpretation, which means, to comprehend experiences, data, events,
judgments, rules and so on
Ennis,
, p.
.
The second component of critical thinking is analysis, which means to identify
the relationships among events, concepts and the other forms of judgments
Demir,
.
The third component is inference, in the sense of drawing conclusions based on
the evidence available (Demir, 2006; Rudd & Baker, 2000).
The fourth component is explanation, defined as to justifying an explanation in a
coherent, methodical, reasonable way (Lowy, 2014).
The fifth component is evaluation which is defined by Facione as the ability to
assess the logical strength of the actual or intended inferential relationships among statements,
descriptions, questions, situations and so on’’.
The sixth component of critical thinking is self-regulation, which is defined as
self-consciously to monitor one’s cognitive activities
Facione,
, pp. -5).
Although teaching critical thinking does not guarantee better life conditions for
learners in the future, knowing critical thinking enables them to make more accurate
decisions affecting the future. Making accurate decision about their lives may make
them happier. Teaching critical thinking needs time and effort, and knowing students
learning styles makes teaching critical thinking easier (Rayneri, Gerber & Wiley, 2006).
Furthermore, knowing a student's learning style and teaching according to the student's
learning style help the teacher engage students with the subject (Boydak, 2008; Rudd &
Baker, 2000).
2.2 Learning Styles
In the last thirty years, many different learning style models have been set; however,
Rita Dunn was one of the first to use the concept of a learning style (Dunn & Dunn,
. ‚ccording to Dunn, ‛eaudry and Klavas
learning styles are learners
dominant behaviours during their learning process. Learning styles indicate
individual's preferred way of learning or how the individual acquires information
an
Felder &
Brent, 2005, p. 59).
Kolb s learning style model is based on his experiential learning theory. Kolb
(1981) regards learning styles as the ways in which learners approach learning, and
argues that individuals tend to have a preferred learning style. According to Kolb (1984,
p. 74), the learning process consists of a four-stage learning cycle. The first stage is
Concrete Experience
(CE-feeling). At this stage, the learner encounters a new
situation or reinterprets an existing experience. This provides the basis for the second
stage called Reflective Observation (RO-watching). The learner understands ideas
and situations from different points of view; in other words, an individual learner looks
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at ideas and situations from several points of view as part of the learning process. In the
learning process, the learner relies on patience, objectivity, and careful judgment
(Suliman, 2006). Reflective observation is required for ‚bstract Conceptualization
(AC - thinking). In this stage, learning involves using theories, logic and ideas, rather
than feelings, to understand problems or situations. Typically, a learner relies on
systematic planning and develops theories to solve problems. In the final stage the
learner applies this new knowledge to a new situation this is called
‚ctive
Experimentation (AE- doing) (Smith & Kolb, 1986). When a learner enters the learning
process, these four stages are experienced and each stage of the learning cycle is
associated with a learning mode - diverging, assimilating, converging, and
accommodating (Svinicki & Dixon, 1987).
A. Diverging Style: Divergers are sensitive and good observers. They like exploring
new ideas. They behave naturally and use imagination to solve problems. They are the
best of all the learning mode groups at viewing concrete situations from several
different viewpoints. Kolb called this style Diverging because people who use this
style perform better in situations that require the generation of ideas (Baymeyer, 2004;
Kolb & Kolb, 2005). Divergers have Concrete Experience/Reflective Observer (CE/RO)
characteristics (Kolb, 1984).
B. Assimilating Style: Assimilators prefer to be concise and to have a logical approach.
Ideas and concepts are more important than people. They prefer good, clear
explanations rather than practice or application. Assimilators excel at understanding
wide-ranging information and organizing it in a clear, logical format (Svinicki & Dixon,
1987). They are primarily interested in ideas, abstract concepts and numbers.
Assimilators have Abstract Conceptualization/Reflective Observation (AC/RO) as
dominant learning abilities (Kolb, 1984).
C. Converging Style: Convergers can solve problems and use their knowledge to find
solutions to problems. They like technical tasks and are prone to analytical thinking;
however, they generally have difficulties in looking at problems from different
perspectives. Convergers like experimenting with new ideas (Kolb, 1984). Their
learning style is classified as Abstract Conceptualization/Active Experimenter (AC/AE).
D. Accommodating Style: The Accommodating learning style is 'hands-on', and
accommodators rely on intuition rather than logic. These learners use other people's
analysis, and prefer to take a practical, experiential approach (Koob & Funk, 2002). They
prefer to work in teams to complete tasks. They do not like to work systematically and
tend not to think analytically. Accommodators have Concrete Experience/Active
Experimentation (CE/AE) as dominant learning abilities (Kolb, 1984). A student's mode
of learning is one of the important factors that define the instruction given. As gifted
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students preferred modes of learning differ from their peers, the teaching methods
should be different from the methods used in general education Eriş,
Dyer,
Myers &
. Indeed, applying teaching methods appropriate to students learning style
improves students achievements ‚lnesyan,
Ghazivakili, et al.,
14; Mahmoud,
2012).
2.3 Gifted Education
Giftedness and gifted education are not old concepts in education and educators have
tried to define the term gifted. The term gifted is generally defined as extraordinary
mental performance in children or as higher learning ability in children when compared
with their peers (NAGC, 2010). Gifted students generally process information faster
than average-ability peers in complex tasks (Roberts, et. al., 1988). When these
definitions are considered, it is supposed that gifted children should have an aptitude
to think critically. However, the level of critical thinking skill varies between gifted
students because of individual differences (Kettler, 2014).
The 1970s and 1980s were a period when special curriculums, aiming at
developing creativity and critical thinking, were designed for gifted education (Tuttle,
Becker & Sousa, 1988). During the same period, some new instructional designs were
also prepared in the light of research on the characteristics of the gifted (Reis & Mc
Coach, 2000). One of the individual characteristics examined in the research was
learning style and another was the critical thinking skills of the gifted. Later, many
studies (Dixon et al., 2004; Mahmood, 2012; Schenck & Cruickshank, 2014; Siriopoulos
& Pomonis, 2007; Suliman, 2006) looked for relationships between learning style and
critical thinking in different fields, such as nursing, engineering, agriculture, primary
education İşlekeller,
Kettler,
. However, only a very limited number of
studies dealt with the relationship between teaching critical thinking and the learning
styles of gifted children.
Although similarities in teaching approaches in gifted education can be
described, there are no general hands on activities suitable for every gifted student
(Ford, Grantham & Whiting, 2008; Ross, & Wright, 1987) because of their having
different cognitive levels and coming from different cultural, socio-economic
backgrounds. In these circumstances, it became a priority for educators across the world
to define gifted students learning preferences in their own cultures. Roberts, et al.
(1988) indicated that as gifted students have different learning preferences than their
peers, an enriched curriculum should be applied in regular classes and this enriched
curriculum should contain differentiated activities in learning experiences, to help
gifted students manage independent study, develop strategies for cooperative learning
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and to participate in interdisciplinary activities (Dixon, et al., 2004; Ketler, 2014; Van
Tassel-Baska, 1992).
2.4 Gifted Education in Turkey
In Turkey, gifted education has flourished during the last two decades. The new model
for gifted education has been given a place in the Turkish education system. In this
model, students who show signs of being talented during their compulsory education
are identified by their teachers. These students are then given two tests. The first is a
multiple-choice exam and students who pass then have a right to take a performance
test. In the performance test, students IQ levels are determined by Wisc-R, WNW or
similar IQ tests whose reliability and validity analyses are made based on a Turkish
sample. Students who are gifted attend a Science and Art Center (SAC) at least two
days in a week (MNE, 2015). At an SAC, they attend courses according to their defined
abilities and study with a mentor up to end of their compulsory education period at the
age of 18.
The SACs' standards for gifted education advise that to work effectively with
identified gifted students, educators need to understand the characteristics of the gifted.
‚s differentiated and enriched curriculums are applied at S‚Cs, knowing students
learning styles helps teachers when teaching critical thinking. There is an emphasis on
developing critical thinking in both the gifted education program and in the general
education program. This study examines whether critical thinking skill levels should be
considered when designing differentiated learning activities, and whether this enables
educators and curriculum designers to compare the effects of cultural differences in
gifted education. Defining gifted students critical thinking skills and learning styles
helps to decide upon the nature of gifted education programs. Although the results
cannot be generalized to all cultures, it can provide results that are comparable with
those of many other cultures. The results may also be helpful in preparing instructional
designs for gifted students in other cultures.
3. Method
3.1 Participants
The sample of the study consisted of 225 gifted and talented students voluntarily
attending two different SACs. In Turkey, there are 83 SACs. 129 (57.3%) of the
participants were male and 96 (42,7%) were female. 53 students (23,6%) were 5th grade,
45 (20%) 6th grade, 35 (15.6%) 7th grade and 92 (40.9 %) 8th graders. Their attendance
duration at an SAC varied. 50 (22.2%) of them had been attending an SAC for 1 year, 58
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(25.8%) students had been attending an SAC for 2 years and 117 had been attending an
S‚C for or more years. Students ages varied from
to
years.
3.2 Data Collection Tools
The present study used the Critical Thinking Skills Scale (CTSS) developed by Demir
(2006). The CTSS measures six dimensions of critical thinking. Of these, three
dimensions, the analysis, evaluation and inference dimensions, are related to giving
answers as true or false in certain situations. For the evaluation dimension, answers
were coded as
for correct answers and
for incorrect answers. The analysis
dimension consisted of eight items, the evaluation dimension had nine items and the
inference dimension had eight items.
Scores for the interpretation and explanation dimensions were based on both
four multiple-choice tests. Questions were based on a single text that was used for both
tests. In the interpretation dimension, there were 10 items and in the explanation
dimension, there were nine items. The last dimension, self-regulation, consisted of 12
items. These dimensions were coded with a Likert-type scale, as 1=never, 2= sometimes
and 3= always.
The scale used in this study was developed by Demir
. In Demir s
research, the Pearson correlation values were: .71 for the analysis dimension; .86 for the
evaluation dimension; and .70 for the inferences dimension. The K-20 values were: .76
for the interpretation dimension; .77 for the explanation dimension; and .99 for the selfregulation dimension (Demir, 2006). In the present study, the Pearson correlation values
were found to be: .74 for the analysis dimension; .83 for the evaluation dimension; and
.74 for the inferences dimension. Reliability values were found to be: .81 for the
interpretation dimension; .88 for the explanation dimension; and .89 for the selfregulation dimension.
The Kolb Learning Style Inventory III was used to define gifted students'
learning styles. The inventory, consisting of 12 items, was developed by Kolb (1985) and
adapted by Gencel (2007) to Turkish culture. In the Gencel inventory, individuals are
tested on how they would respond in 12 different formal learning situations. For each
situation, participants are asked to choose from 4 possible learning approaches. In the
present study, participants were asked to grade the situations
according to their
personal preferences on a scale of 1 to 4, where 4 was for the most suitable situation,
and 1 was for the least suitable situation. Analyzing their responses, students learning
styles were distributed into four categories as concrete experience' CE , 'reflective
observation' RO , abstract conceptualization' ‚C and active experimentation' ‚E .
The AE score was subtracted from the AC score and the RO score was subtracted from
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the AE score, and the results were analyzed according to the scale. At the end of the
process, students were labeled as accommodating , diverging , assimilating , or
converging'. In addition, the distribution of the students was determined according to
the Nine-Region Learning Style Type Grid (Hunt, 1987).
The same scale has been used in many studies in Turkey, including Demirbaş &
Demirkan, 2007; Ekici, 2013; and Tezci & Ataseven, 2016. In the present study, the
following Cronbach Alpha reliability coefficients were found: .76 for the CE dimension;
.71 for the RO dimension; .78 for the AC dimension; .84 for the AE dimension; .85 for
AC-AE; and .79 for and AE-RO. These values are similar to the values found by Kolb
. They are higher than the values found in a previous adaptation study by ‚şkar
and Akkoyunlu (1993), but are similar to that of Gencel (2007).
3.4 Data Analysis
Descriptive statistics were used to identify the participants levels of critical thinking
and learning styles. The Chi-Square and t test were conducted to examine gender
differences. An ANOVA test was conducted to examine whether participants
responses differed by their learning styles. A Pearson correlation analysis was carried
out to examine the inter-relations between the variables of interest. Finally, a regression
equation block method was conducted to examine the relative contribution of the
factors predicting critical thinking.
4. Findings
4.1 Descriptive Analysis of Learning Styles
Students learning style scores' Mean M , Standard Deviation SD and Range R
values were analyzed for each item (CE, RO, AC, AE, AC-CE and AE-RO). In addition,
students favorite learning styles were defined. The results were shown in Table 1.
Table 1: The average raw scale scores and preferred learning styles
R
M
SD
Skewness
Kurtosis
CE
44-14
23.47
5.95
.885
1.004
RO
43-16
29.11
4.98
.138
.471
AC
48-20
33.82
6.48
.512
-.184
AE
45-18
33.60
5.72
-.182
-.191
AC_CE
34 to -14
10.35
10.65
.210
.044
AE_RO
26 to -19
4.49
8.59
-.393
.128
Preferred
Accommodating
Diverging
Assimilating
Converging
Styles
(n=36. 16%)
(n=56. 24.9%)
(n=77. 34.2%)
(n=56. 24.9%)
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According to the analysis, the lowest mean (M = 23.47, SD = 5.95) was seen in the CE
dimension and the highest mean (M = 33.82, SD = 6.48) was seen in the AC dimension.
According to the scores obtained on the learning style scale, the assimilating learning
style was the most preferred style (34.2%). Other preferred styles were the converging
learning style (24.9%), the diverging learning style (24.9%) and the accommodating
learning style (16%).
4.2 Analyses of learning styles according to demographic variables
A Chi-square analysis was conducted to define whether students learning styles
change according to gender and duration of attendance at an SAC.
Table 2: The distribution of gender and duration of attendance at a Science and Art Center
by Learning Style
Gender
Duration of Attendances to SAC
Male
Female
1 Year
2 Year
3 and more years
Accommodator
16
20
9
8
19
Converger
30
26
5
8
43
Diverger
32
24
8
13
35
Assimilator
51
26
11
13
53
Total
129
96
33
42
150
There was no significant difference in learning styles by gender and duration of
attendance at an SAC (gender, X2= 5.263, df=3, p=.154; attendance duration at an SAC,
X2= 7.564, df=6, p=.272).
4.3 Descriptive Statistics of Critical Thinking
The highest possible scores on three sub-dimensions of the critical thinking scale were 8
for 'evaluation', eight for 'inferences', 10 for 'interpretation' and 9 for 'explanation', and
the lowest possible score was 0 on all three sub-dimensions. For the self-regulation
dimension, the highest possible score was 36 and the lowest possible score was 12.
Descriptive statistics of the scores taken from critical thinking scale and sub
dimensions are given in Table 3.
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Table 3: Descriptive statistics for the critical thinking scale and its sub-dimensions
N
Minimum
Maximum
Mean
Std. Deviation
Skewness
Kurtosis
Analysis
225
5.00
8.00
7.27
.97
-1.020
-.252
Evaluation
225
5.00
9.00
8.18
1.07
-1.069
-.003
Inferences
225
4.00
8.00
7.08
1.05
-.887
-.382
Interpretation
225
3.00
10.00
7.75
1.72
-.364
-.564
Explanation
225
4.00
9.00
7.51
1.35
-.531
-.835
Self-regulation
225
20.00
36.00
30.18
4.14
-.703
-.083
Entire Scale
225
7.67
13.33
11.33
.08
-.524
-.030
According to the analysis, the lowest mean (M = 7,08, SD =1.05) was seen in the
interpretation dimension and the highest mean (M = 8,18, SD = 1.07) was seen in the
evaluation dimension. The mean scores on the sub-dimensions and on the overall
critical thinking scale were high. That is to say, gifted students had a high ability to
think critically.
4.4 Analysis of Variance Critical Thinking Skills by Learning Styles
ANOVA was conducted to define the relationship between preferred learning styles
(LS) and critical thinking skills. The results are given in Table 4.
Table 4: ANOVA Results for Critical Thinking Skills According to Learning Styles
CTT
LS
N M
SD
Analysis
a-Accommodators
36 7.06
1.01
b-Convergers
56 7.48
.83
c-Divergers
56 7.14
1.08
d-Assimilators
77 7.30
.96
a-Accommodators
36 7.69
1.19
b-Convergers
56 8.43
1.00
c-Divergers
56 8.11
.98
d-Assimilators
77 8.27
1.08
a-Accommodators
36 6.67
1.04
b-Convergers
56 7.30
.97
c-Divergers
56 6.89
1.17
d-Assimilators
77 7.23
.97
a-Accommodators
36 6.44
1.50
b-Convergers
56 8.55
1.42
c-Divergers
56 6.98
1.55
Evaluation
Inferences
Interpretation
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Mean Square
F(3. 221)
p
Tukey
1.714
1.814
.146
-
4.302
3.834 .011* .21
a<b.d
4.242
3.956 .009* .20
a<b.d
51.989
22.770 .000* .54
a.c<b.d
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Explanation
Self-regulation
Entire
d-Assimilators
77 8.32
1.54
a-Accommodators
36 7.11
1.32
b-Convergers
56 7.73
1.33
c-Divergers
56 6.93
1.36
d-Assimilators
77 7.95
1.19
a-Accommodators
36 27.58 4.71
b-Convergers
56 31.14 4.00
c-Divergers
56 29.63 3.73
d-Assimilators
77 31.09 3.71
a-Accommodators
36 10.42 1.24
b-Convergers
56 11.79 1.09
c-Divergers
56 10.95 1.14
d-Assimilators
77 11.68 1.06
14.065
8.492 .000* .32
a.c<b.d
125.264
7.975 .000* .27
a<b.d
19.734
15.698 .000* .42
a.c<b.d
*p<.05
According to the results, there were no significant differences on the Analysis
dimension (F= 1.814, p<.05) in terms of learning style. On the evaluation dimension,
accommodators had lower scores (M= 7.69, SD=1.19) than convergers (M= 8.43, SD=1.00)
and assimilators (M=8.27, SD=1.08) (F=3.834, p<.05). On the inferences dimension,
accommodators (M= 6.67, SD=1.04) had lower scores than convergers (M= 7.30, SD=.97)
and assimilators (M=7.23, SD=.97) (F=3.956, p<.05). On the interpretation dimension,
students preferring accommodating (M= 6.44, SD=1.50) and diverging learning styles
(M=6.98, SD= 1.55) had lower scores than students who preferred converging (M= 8.55,
SD=1.42) and assimilating (M=8.32, SD=1.54) learning styles. On the Explanation
dimension, accommodators (M= 7.11, SD=1.32) and divergers (M=6.93, SD= 1.36) had
lower scores than convergers (M= 7.73, SD=1.33) and assimilators (M=7.95, SD=1.19)
(F=8.492, p<.05). On the self-regulation dimension, students preferring accommodating
learning styles (M=27.58, SD=4.71) had lower scores than students preferring
converging (M=31.14; SD= 4.00) and assimilating (M=31.09; SD=3.71) learning styles.
According to the overall scores on the critical thinking skills scale, convergers (M=11.79,
SD=1.09) and assimilators (M=11.68, SD=1.06) had higher scores than accommodators
(M=10.42, SD=1.24) and divergers (M=10. 95, SD=1.14) (F=15.698, p<.05). Analysis of the
means on the interpretation dimension and overall scores on the critical thinking scale
revealed a large effect size, for the rest of the dimensions the effect size was found to be
at a medium level (Cohen, 1988). This result shows that learning styles had an
important effect on critical thinking skills.
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4.5 Correlation and Regression Analysis
Correlation analysis was carried out to determine whether the gifted students critical
thinking skills (both the overall score and the sub-dimension scores on the critical
thinking scale) were associated with CE, AC, AE, and RO modes of learning. The results
of the analysis are provided in Table 5.
Table 5: Correlational Analysis
1
1-Analyses
2
3
4
5
6
7
8
9
10
.43**
3-Inferences
.42** .59**
4-Interpretation
.32** .35** .30**
5-Explanation
.39** .34** .31** .54**
6-Self-regulation
.43** .47** .47** .22**
.37**
.63** .67** .64** .58**
.65**
.87**
8-CE
.52** .35** .31** .41**
.47**
.46**
.59**
9-RO
.31** .36** .34** .40**
.31**
.38**
.49** .52**
10-AC
.38** .66** .56** .33**
.38**
.58**
.68** .49** .46**
11-AE
.27
.31
.43
.46** .55** .44**
.48**
.14*
-.03
Entire
**
.30
**
.38
**
.19
**
12-AE-RO
.05
.06
-.01
.06
13-CE-AC
.05
-.05
-.08
-.02
-.08
-.11
-.12
-.10
Gender
12
1
2-Evaluation
7-Critical Thinking
11
**
.01
**
-.02
.04 -.14*
-.21
**
.03 .19**
-.04 .25**
-.11 -.19
*
.06
-.38**
-.04
-.24**
.05 -.04
.11
-.12
-.16* -.03
**Correlation is significant at the 0.01 level (2-tailed).
*Correlation is significant at the 0.05 level (2-tailed)
Analysis of the results showed that there was a positive medium level of correlation
between the scores taken from the sub-dimensions of the critical thinking scale and the
learning styles inventory scores. The lowest correlation was found between the
interpretation dimension of the critical thinking scale and the AE mode of learning
(r=.19, p<.01). The highest correlation was found between the evaluation dimension of
the critical thinking skills scale and AC (r=.66<.01). There was also a positive medium
level of correlation with a significant meaningful difference among all variables (p<.01).
There was no significant relationship between AE-AC, CE-AC and the sub dimensions
of the critical thinking scale. There was a medium level correlation between overall
critical thinking scores (r=.59, p<.05) and RO (r=.49, p<.05), AC (r=.68, p<.05), AE (r=.46,
p<.05). As there was no autocorrelation between learning style row scores, a forced
hierarchical regression analysis and stepwise method was applied to determine which
variables served as predictors for the sub-dimensions of the critical thinking scale. In
the analysis, the sub-dimensions of critical thinking were dependent variables. The
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predictor variables included in the equation were as follows: the first block consisted of
the demographic variable (gender as a dummy variable) and the second block consisted
of the mode of learning followed by CE, AC, RO and AE. The results are presented in
Table 6.
Table 6: Regression Analysis
Dependent
Variable
Model 1
Analysis
Evaluation
Model 2
Inferences
Model 1
Model 1
Model 2
Interpretation
Model 1
Model 2
Model 1
Model 2
Explanation
Model 3
Model 1
Self-regulation
Critical
Thinking
Entire
Model 2
Model 1
Model 2
Model 3
Model 4
Independent
Variables
CE
CE
AC
AC
AC
AC
AE
CE
CE
RO
Gender
Gender
CE
Gender
CE
AC
AC
AC
CE
Gender
Gender
AC
Gender
AC
CE
Gender
AC
CE
RO
β
.517
.439
.159
.656
.556
.487
.145
.406
.274
.205
-.213
-.241
.488
-.218
.412
.152
.581
.470
.226
-.190
-.108
.666
.091
.582
.365
-.172
.436
.308
.156
Std.
Error
.006
.006
.009
.007
.007
.008
.007
.011
.013
.018
.137
.119
.007
.120
.008
.013
.036
.040
.027
.134
.100
.009
-.152
.009
.006
.091
.010
.007
.009
t
p
R
R2
df
F
9.025
6.752
2.451
12.990
10.001
7.768
2.319
6.639
3.929
3.662
-3.249
-4.230
8.577
-.218
6.326
2.316
10.653
7.727
3.712
-2.891
-2.199
13.579
-3.393
9.408
7.165
-3.859
8.244
5.730
2.927
.000
.000
.015
.000
.000
.000
.021
.000
.000
.000
.001
.000
.000
.000
.000
.021
.000
.000
.000
.004
.029
.000
.001
.000
.000
.000
.000
.000
.004
.517
.268
1, 223
81.454
.536
.280
2, 222
44.645
.656
.556
.571
.431
.310
.326
1, 223
1, 223
2, 222
168.742
100.024
17.748
.406
.165
1, 223
44.079
.462
.213
2, 222
29.970
.213
.532
.045
.283
1, 223
2, 222
11.115
43.772
.548
.300
3, 321
31.543
.581
.334
1, 223
113.481
.613
.370
2, 222
66.881
.190
.688
.036
.473
1, 223
2, 222
8.355
99.807
.757
.573
3, 221
98.741
.767
.589
4, 220
78.735
In the stepwise regression analysis, CE, AC, RO and AE served as the predictor
variables.
The
dependent
variables
were
Analysis,
Evaluation,
Inferences,
Interpretation, Explanation, Self-regulation and the Overall Critical Thinking Scale. It
was found that each mode made an independent contribution to the equation. The
values were as follows ‚nalysis dimension, CE β = .
dimension, ‚C β = .
Inferences dimension, ‚C β = .
Interpretation dimension, CE β = .
= .
and ‚C β = .
and ‚C β = .
and RO β = .
and ‚E β = .
Explanation dimension, CE β
Self-regulation dimension, ‚C β = .
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, Evaluation
and CE β = .
.
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It was found that ‚C β = .
, CE β = .
and RO β = .
predicted the
Critical Thinking Scale entire score and it emerged that AC alone accounted for 46.2% of
the variation in General Critical Thinking Skills. The other variables (CE and RO),
collectively, accounted for an additional 9.3% of the variation in the Critical thinking.
AC and CE were the best predictive of critical thinking. Gender, accepted as Dummy
variable, was a predictive in Explanation dimension β= -0.22) and overall of the critical
thinking scale β= -0.17). For other sub dimensions, gender had no contribution to the
equation.
5. Conclusion and Discussion
The purpose of this study is to identify whether mentally gifted students critical
thinking skills differentiate according to their gender, duration of attendance to SAC
and preferred learning styles, and which modes of learning (CE, AC, RO and AE)
predict critical thinking skills. The results showed that the predominant style was
assimilating, followed by converging, diverging and accommodating.
Research conducted on different populations revealed varying results. Yenice
(2012), Ghazivakili, et al. (2014) and Tulbure (2012) found that the predominant learning
style were convergent followed by assimilating. In some studies, the predominant
learning style was found to be diverging (Andreou, Papastavrou & Merkouris, 2014;
Gyeong & Myung, 2008; Siriopoulos & Pomonis, 2006). Other studies found that the
assimilating style was predominant (Yamazaki, Murphy & Puerta, 2002; Patterson,
1994, Tezci & Ataveseven, 2016), while yet others found the accommodating style to be
predominant (Colucciello, 1999; Nastanski & Slick, 2011). Kolb (2005) defined the
converging learning style as the predominant learning style in his study. Ross & Wright
(1987) argued that since gifted students are different from non-gifted students, the
teaching of gifted students should be different from the teaching of students attending
general education, and that gifted students learning styles and cognitive characteristics
are not the same as those of non-gifted students.
In this study, the preferred learning style was found to be the assimilating style,
followed by the converging and diverging styles in joint second place. In Turkey a
number of studies ‚y, Padem & Eriş,
G(ven & K(r(m,
Tezci & ‚taseven,
2006) found that the preferred learning style was the assimilating style. These findings
are contrary to Demirbaş and Demirkan s
study in which they state that the
dominant learning style is converging. On the other hand, in other studies related to
students learning style preferences there were different results e.g. Colucciello,
Cook, n.d.; Kolb, 1993; Kvan & Yuan, 2005; Mahmoud, 2012). Mahmoud, in a study
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(2012, p. 409) based on research group characteristics (e.g. gifted or non-gifted, young or
adult, teacher or student, student enrolled in department, and cultures indicated that,
students preferred learning styles might be influenced by culture because of different
socialization . Results from previous research e.g. ‛echer & Trowler,
Kolb,
,
indicated that students learning styles changed according to the subject being
studied.
The results in the present study indicate that gender and duration of attendance
at an SAC were not significant variables in preferred learning styles. Gender was not a
significant variable in gifted student s preferred learning styles. Learning styles have
stable characteristics and develop regardless of the subject being studied or the skill
being mastered (Kolb, 1981; Nastanski & Slick, 2011; Ross & Wright, 1987). The finding
about gender agrees with many studies Demirbaş & Demirkan,
Myers & Dyer,
2006; Walsh & Hardy 1999; Yenice, 2012), but contradicts other studies (Ghazivakili, et
al, 2014; Peng, Ma & Li, 2006; Philbin, Meier, Huffman & Boverie, 1995; Wehrwein,
Lujan & DiCarlo, 2007).
In the current study, gender was not a significant variable in the terms of
learning styles, but it was found to be related to thinking skills. Gender served as a
predictor of overall thinking skill disposition scores and for the
explanation
dimension. Gender was not a significant predictor for other sub dimensions of the
critical thinking scale. Some studies (Rudd, Baker & Hoover, 2000; Torres & Cano,
1995b; Walsh & Hardy, 1999) indicate that gender was a significant variable in critical
thinking skills, however. Nevertheless, other studies (Kettler, 2014; Myers & Dyer, 2006)
of gifted students critical thinking skills found that there was no relationship between
gender and critical thinking skills.
In this study, gifted students critical thinking scores for both the overall score on
the critical thinking scale and the scores on its sub dimensions were high. The highest
mean in the sub dimensions was seen in the evaluation dimension. This implies that the
gifted education program (SAC program) in Turkey is sufficient for developing gifted
students critical thinking skills. The S‚C program contains learner-centered learning
methods such as problem based learning, discussion, brain storming, individual and
small group projects. These methods include innovative applications based on students'
learning. Students have control over their own learning process and actively construct
their own knowledge. Teaching and learning applications based on such approaches
contribute to developing critical thinking skills (Ross & Wright, 1987; Salehi, 2007;
Willingham, 2007). A study comparing gifted and non-gifted students by Kettler (2014)
showed that gifted students had a higher capacity than non-gifted peers for critical
thinking.
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The results of the present study indicate that there is a relationship between
critical thinking and preferred learning styles except in the analysis dimension. There
were significant differences between the four learning styles compared with the overall
score of critical thinking disposition and the sub dimensions (evaluation, inferences,
interpretation, explanation and self-regulation). The mean scores of the assimilating and
converging groups were higher than those of the diverging and accommodating
groups. In other words, the overall score of critical thinking and sub dimensions, except
for analysis, were correlated with the assimilating and converging styles preferred by
gifted students.
Gifted students preferring assimilating and converging learning styles were
better critical thinkers than divergers and accommodators. This finding is consistent
with other studies conducted in different countries (Colucciello, 1999; Myers & Dyer,
2006; Suliman, 2006 .The results of the present study, however, indicate that students
learning styles may play an important role in critical thinking.
Learning style is relatively stable and develops according to the student's field of
education (Wong & Nunan, 2011). When the sample group is considered to be
cognitively highly able, it can be said that students with high cognitive capacity prefer
mostly assimilating and diverging learning styles. Kolb
asserted that there was
a relationship between assimilating learning style and thinking skills. He also indicated
that there were relationships between the organization of knowledge, building
conceptual structures, testing ideas and theories and analyzing the data.
Ghazivakili, et al (2014) found a significant difference between the evaluation ,
inductive reasoning and critical thinking skills according to students' thinking styles.
In the present study, convergers had higher scores on the critical thinking scale than
other learning styles. Wessel and Williams (2004) found similar results in their study of
Master s entry-level students. Although, there are many studies (Durukan & Maden,
2010; Güven & Kürüm, 2007; Myers & Dyer, 2006; Torres & Cano, 1995; Yenice, 2012)
indicating the relationships between learning style and critical thinking, there are some
other studies (Ay, et. al., 2010; Rudd et al., 2000) that indicate the reverse. This
discrepancy may be caused by how the sample group was selected and by the use of
different tools to define learning styles. Kettler (2014) found a significant difference
between gifted and non-gifted students. Another reason for the discrepancy in the
results of these studies is that only Kettler's studied gifted students. Gifted students
apply a wide variety of different strategies in problem solving, decision making,
assessing their performances compared with their non-gifted peers (Kettler, 2014).
Thus, gifted students' preferred learning styles may be different from those of nongifted peers.
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In this study, a positive correlation was found between the overall scores of
critical thinking disposition, its sub-dimensions and modes of learning. The highest
correlation was found between the entire critical thinking skills scale score and the AC
mode of learning. The lowest correlation was seen between the analysis dimension of
the critical thinking scale and the AE mode of learning. There was a medium level of
positive correlation between all variables. Toress and Cano (1995), Pai and Eng (2013)
found similar results.
According to the regression results, gender was only a predictor variable in the
explanation and the self-regulation dimensions and the overall score of critical thinking
disposition. In the analysis dimension, the CE mode of learning was the most positive
predictor of critical thinking disposition, followed by the AC mode of learning. The AC
mode of learning was the only significant predictor in the assessment dimension of
critical thinking disposition. In the inferences dimension, the AC mode of learning
made a high contribution to variance. In the interpretation dimension, the CE and the
RO modes of learning were predictive. In the explanation dimension, except for gender,
the CE mode of learning was the best predictor, followed by the AC mode of learning.
In the self-regulation dimension, the AC mode of learning was the best predictor
variable, this was followed by the CE mode of learning. The AC mode of learning made
the highest contribution to the variance for the critical thinking disposition except for
gender, respectively the CE and RO modes of learning. The current study shows that
although the most predictive mode of learning was AC, other modes of learning were
predictive of the critical thinking disposition. According to Kolb (1984), learning styles
are not fixed personal traits. Thus, teaching should be based on learning preferences
rather than on learning styles and all modes of learning should be taken into
consideration in the teaching and learning process.
It is clear that gifted students have different cognitive characteristics. Although
learning styles are affected by personal characteristics, environmental factors are also
considered to be important factors in the development of learning styles (Kolb, 1981;
1984), and the design of teaching is an important influence on gifted students learning
styles. A study by Mills supports Kolb's (1984) theory, in that analytical thinking,
evaluation, critical thinking, clarity and flexibility are associated with the AC mode of
learning Mills,
. Kolb s learning theory was based on a four-stage learning cycle.
In this respect, Kolb s experimental learning theory works on two levels a four-stage
cycle of learning and four separate learning styles (Kolb, 1984).
Yamazaki, et. al. (2002) indicated that the AC and CE mode of learning
preferences were related to higher order thinking. Similarly, Suliman (2006) found a
positive correlation between critical thinking skills and AC, CE modes of learning, and
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a negative correlation between critical thinking skills and the CE and RO modes. In
some studies (Lowy, 2013; Mahmoud, 2012; Nasrabady, et al. 2012) that used different
learning styles scales, a relationship was found between critical thinking and learning
styles.
As learning styles offer a framework related to strong and weak aspects of
students learning processes Kolb,
, the ‚C, CE, RO and ‚E modes of learning
should be taken into consideration in the teaching and learning process for developing
gifted students critical thinking skills. The ‚C mode of learning was predictive for all
critical thinking skills on the scale except for the interpretation dimension. In the
current study, the AC mode of learning was the strongest and most prevalent predictor.
Therefore, in an educational setting, gifted students should perhaps be frequently
engaged in creating theories to explain observations. This mode is related to abstraction
and analytical skills, which are important skills in devising a theory (Kolb, 1981).
Gifted students like learning on their own and they will do just fine on their own
(Ross & Wright, 1987, p. 50). This is evidence for their being independent learners and
teachers should consider this characteristic when teaching them. Furthermore,
developing students perceived efficacy and their regulation of self-aims should be
taken into consideration during teaching and learning. The results of this study also
indicate that the curriculum applied in gifted education centers should be based on
activities. Purvis (2009) indicated that curriculum and instructional design had a great
effect on teaching critical thinking. Thus, it may be helpful to consider students
learning modes when designing curriculums and teaching and learning experiences.
When the nature of the gifted students preferred learning styles are considered
(Kolb, 1984), it seems clear that their learning environment should support their being
independent learners and help them to control their own individual learning process.
The curriculum should also be enriched in accordance with these findings. Teachers'
consideration of learning styles during gifted students education will contribute to the
development of gifted students' critical thinking skills.
6. Limitations of the Study
The current study was conducted with only 225 participants. Furthermore, the
participating students could not be divided into age categories because although
students age was the same, their attendance duration at SACs varied. Comparing the
results of general education students critical thinking skills with those of gifted
students will help us to understand their learning styles and to create better learning
opportunities for gifted students.
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7. Future Direction
Conducting similar studies with larger samples and comparing the results with studies
of non-gifted students will give better results that will help to generalize the findings.
Searching for other variables, such as socio-cultural background and their past learning
experiences during the compulsory education period, and their effects on gifted
students critical thinking skills will shed light both on how to understand and on how
to develop their critical thinking processes.
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