Learning style theory as a potential tool in guiding student choice of college major.
Terregrossa, Ralph A. ; Englander, Fred ; Wang, Zhaobo 等
INTRODUCTION
There has been considerable interest in the last thirty years
concerning the role of students' learning styles in explaining
variations in individual student cognitive achievement. Proponents of
various learning style theories have argued that it is important to
match the mode of instruction with the learning style preferences of
students to facilitate and enhance greater cognitive achievement (Dunn,
2000).
Another potential approach to understanding the relevance of
learning styles to the education process is to examine the extent to
which students make their choices of an academic major, and therefore to
prospective career paths, based on their particular pattern of learning
style preferences. This study examines whether or not, among the 3,533
incoming freshmen at a major urban university in the northeast region of
the U.S. for the academic years beginning in 2004, 2005 and 2006,
students who chose a particular major had a learning style profile which
is different from the profile of students who selected other academic
majors. Students' learning style profiles utilized in this study
are identified using the Building Excellence (BE) (Rundle & Dunn,
2009a) survey instrument which is based on the Dunn and Dunn learning
style model (DDLSM) (Rundle & Dunn, 2009b).
To the extent that a particular pattern of learning style
preferences is associated with a particular academic major, that
information may potentially be utilized to more effectively guide
prospective and existing college students in their choice of academic
majors. The empirical results from this current study could be used by a
university's counseling staff to inform an individual student
whether his or her learning style profile is similar to other students
who have chosen a particular major or whether the student's
learning style profile is a closer match to students who have chosen
another or several different majors. Such information could well be
helpful in counseling students to select an academic major and career
path for which they are best suited.
Another important outcome of this research is to provide the
necessary foundational step in the larger process of examining the
extent to which students who have chosen a particular major with a given
learning style profile are more likely (based on additional follow-up
student data that would be added in subsequent stages of this research
project) to (a) continue in their academic program (i.e., advancing the
student retention goals of the university), (b) maintain the academic
major selected at the outset of the student's academic program, (c)
graduate from the university within a given time interval and (d)
graduate with a grade-point average that reflects well on the
student's overall potential (given other information in the
application materials submitted by the students). That is, this research
may prove to be a key resource for a university in facilitating a
greater retention of students and in allowing university staff to
counsel students so as to help those students make more efficacious
decisions leading to improvements in academic performance and
realization of the students' career potential. However, this more
ambitious potential use of learning style information begins with the
effort to answer the central question posed by the current research: do
students who have selected particular academic majors tend to have
distinct learning style profiles?
LITERATURE REVIEW
Interest in identifying factors that affect college students'
choice of an academic major is increased particularly when there is a
substantial decrease in either the selection of a specific major or the
decline of enrollment in various academic programs. For example, in
response to the steep decline in the late 1990's of college
students who chose to major in accounting, Simons, Lowe and Stout (2003)
found that expectations of potential earnings and prospective employment
opportunities upon graduation were the two most important factors that
affected business students' decision to major in accounting. These
findings were consistent with the results of an earlier study of
business students by Lowe and Simmons (1997). However, Lowe and Simmons
(1997) also found that there were important differences among the
factors that influenced students' decision to major in accounting,
marketing and management.
In reaction to the plunge in the number of students choosing to
major in computer science and information technology in the early
2000's, Crampton, Walstrom, and Schambach (2006) found that the
factors that affected business students' choice of major, in order
of importance, included personal interest in the subject matter,
long-run salary prospects, employment opportunities upon graduation and
starting salary. The authors also found that the least important factors
were the university's advisement center, career services program
and the high school guidance counselors. This finding suggests that
university advisors may not be efficiently utilizing their resources or
may not be utilizing the most useful resources and, in either case,
consequently not adequately serving students.
Reacting to the steep decline in the percentage of college students
majoring in the humanities, Edinboro University of Pennsylvania recently
announced that it was closing degree programs in German, philosophy and
world languages and culture because of a lack of majors. At Harvard
University, the number of students majoring in the humanities had
decreased by twenty percent over the last decade and "most students
who say they intend to major in humanities end up in other fields"
(Lewin, 2013, p. A18). Lamenting the paucity of students majoring in the
humanities, Dan Edelstein of Stanford University said, "while it is
easy to spot the winners at science fairs and robotics competitions,
students who excel at humanities get less acclaim and are harder to
identify" (Lewin, 2013, p. A18).
The authors of the present study hypothesize that it may be
possible to identify students who may have an aptitude for, and thus may
excel in, the humanities or any other academic major. Students enter
college with a unique, multifaceted set of biologically and
experientially determined cognitive skills which influence their
learning styles (Dunn, 2000). If a student's learning style can be
identified, and that student subsequently is advised to specialize in an
accordant academic major in which other students with a similar learning
style historically have been successful, then the advised student's
academic productivity and career opportunities may be enhanced.
The idea that a student's academic productivity can be
enhanced by specializing in a particular major for which he or she has
an aptitude is not original to the authors of this study. Landreth and
Colander (2002) make an inference regarding specialization from Adam
Smith's Wealth of Nations (1776), "At birth, we are all
similarly talented; it is only after we begin to specialize in various
activities that we become more proficient relative to others who do not
specialize," (Landreth & Colander, 2002, pp. 91). Although Adam
Smith most certainly was explaining the benefits associated with the
division and specialization of labor in the context of increased
production of goods, Landreth and Colander (2002) suggest that the
concept applies equally to students' academic production of
knowledge.
Terregrossa, Englander, Wang, and Wielkopolski (2012) analyzed the
differences in learning styles among college students in economics and
accounting courses. Students' learning styles, identified using the
BE (Rundle & Dunn, 2009a) survey instrument, were subsequently used
to explain variations in student achievement. The results indicated that
learning styles had a statistically significant impact on students'
academic achievement. Furthermore, the results showed that the cohort of
economics students in the sample had a different learning style profile
than the cohort of accounting students. The implication is that each
college student embodies a unique and specialized learning style that
allows him or her to be relatively more adept at, and productive in,
learning a specific academic discipline, whether it is biology,
philosophy, accounting or economics, relative to other students with
different learning style profiles.
The methodology utilized by Terregrossa et al. (2012) suggests that
if historical information is available regarding which students
consistently had higher rates of achievement, retention and graduation
in alternative academic majors, as well as students' unique or
specialized learning styles, then that information could potentially
serve as the basis for determining which major is most suitable for a
particular student. If a cohort of students equipped with a particular
learning style is consistently successful in the humanities, for
example, then counselors could more effectively advise a student who
possess a learning style comparable to that of the successful cohort to
consider selecting to major in the humanities. In this way, the
university's administrators can utilize its resources more
efficiently to better serve students.
LEARNING STYLE MODEL
Students' learning styles were identified in the current
research utilizing the BE survey (Rundle & Dunn, 2009a), designed to
reflect the DDLSM (Rundle & Dunn, 2009b) learning styles model. The
DDLSM theorizes that an individual's learning style is composed of
a combination of interrelated perceptual, environmental, physiological,
emotional, sociological, and psychological categories. The perceptual
category includes preferences for alternative perceptual modalities,
including auditory, or learning by listening; visual-picture, or
learning by seeing images, illustrations or pictures; visual-word, or
learning by reading; tactile and/or kinesthetic, or learning through
hands-on experience and by doing; and, verbal-kinesthetic, or learning
by verbalizing.
The environmental category includes preferences for background
sound versus silence, bright or soft light, cool or warm temperature and
formal versus informal seating. The physiological category reflects the
student's ability to remain energized, focused and alert. This
category includes preferences for intake of snacks or drinks while
learning, the time of day when the student does his or her best work,
and whether the student needs to be moving while learning.
The emotional category includes preferences for internal versus
external motivation, persistence, or starting and finishing one project
at a time, conformity to societal norms, and structure, or a preference
for internal or external direction. The sociological category reflects
whether students prefer to work alone, with a partner or with a group of
peers, and whether students prefer to learn with an authoritative versus
collegial adult. This category also reflects whether students prefer to
learn using a variety of methods or by using an established routine. The
psychological category includes the preference for either a reflective
or compulsive approach to making decisions and solving problems. This
category also identifies the students' thought processing method,
hypothesized to include analytic or global processing methods. The
integrated learners have both analytic and global characteristics and
utilize the alternative styles depending on the nature of, and interest
in, the material to be learned.
Analytic learners learn best in a quiet, brightly lighted and
formal (e.g., sitting at a desk) environment. They like to work alone,
tend to be persistent (i.e., prefer to start and finish one project at a
time), and do not snack while learning. They also learn more easily when
details are presented in a sequential, step-by-step manner that builds
toward a conceptual understanding of the idea to be learned.
Global learners learn more easily when they understand the total
concept first then subsequently focus on the underlying details. They
learn best with background sound, soft light in a relaxed environment
(e.g., sitting on a couch or in a coffee shop). They prefer to work with
others, tend not to be persistent (i.e., work simultaneously on several
projects), take frequent breaks, enjoy snacks when learning, and prefer
to be taught with the use of illustrations and symbols. Global learners
prefer new information to be presented anecdotally, especially in a
humorous way that explains how the concept relates to them.
METHODOLOGY
Sample and Data Collection
The BE is an online survey containing 118 self-reflective questions
that are answered on a five-point Likert scale. The BE survey identifies
all twenty-six learning style preferences contained in the six
categories that comprise the DDLSM. For example, the preference for
noise is determined by answering 'strongly agree',
'agree', 'uncertain', 'disagree' or
'strongly disagree' to the following statements:
* I concentrate best in quiet surroundings with no sound or people
talking.
* I concentrate best when there is sound, or when music is playing
in the background.
* I concentrate best in a quiet place--especially when I am working
on difficult tasks.
* I concentrate best with sound in the background when I am working
on difficult tasks.
RESULTS OF THIS STUDY
An analysis of responses to the BE (Rundle & Dunn, 2009a)
survey was conducted in two stages to identify distinguishable
differences among the learning styles of students that chose to major in
the broad academic categories of business, science or social science.
The BE was administered to incoming freshmen in the beginning of the
academic years of 2004, 2005 and 2006, and included 955, 1,597 and 981
students who selected to major in business, science and social science,
respectively, for a total of 3,533 observations.
To establish whether there exists a significant difference in
students' learning styles among the alternative broad categories of
majors, the first stage of the analysis utilized the twotail, pair-wise
t-tests for differences in means between business and science majors,
science and social science majors and social science and business
majors. The results are reported below in Table 1.
In seven of the twenty-six learning style preferences, there were
no significant differences in the mean value of the students'
preferences among the different majors, including perceptual preferences
for visual-picture (pictures, graphs and diagrams vs. printed words) and
tactile/kinesthetic (hands on learning), physiological preferences for
mobility (moving while learning) and late morning (preferred time of day
to learn), the emotional preference of persistence (starting and
finishing one project at a time), and sociological preferences of small
group and team (with whom one prefers to learn). However, for the
remaining nineteen preferences included in all six learning style
categories, there were significant differences in the mean value of
students' learning style preferences among the alternative majors.
These results, consistent with the findings of Terregrossa et al.
(2012), indicate that, although there were some similarities, there are
substantial differences in all six categories of learning style
preferences among the cohort of students in the alternative majors.
However, the differences in students' learning styles do not
coalesce in such a way that differentiates the cohorts as belonging to
any particular academic major category. Thus, the differences among the
students' learning style preferences alone do not provide enough
information to distinguish which students have learning style preference
profiles that are compatible with the business, science or social
science related categories of majors.
Therefore, in the second stage of this analysis, factor analysis
was utilized to differentiate the learning style of the cohort of
students in the alternative major categories. Within each category of
academic majors, factor analysis potentially reduces the cohort's
original preferences to a smaller set of latent factors that coalesce
and reflect the underlying characteristics of the cohort's learning
style. The factors then may be used to distinguish which students
"belong" to the business, science or social science categories
of academic majors. The factors which reflect the learning style
profiles that discriminate among business, science and social science
majors were determined through a procedure which initially applied a
principal components analysis procedure to the learning style preference
variables. The resulting factors are orthogonal (uncorrelated to one
another). This is followed by a Varimax rotational procedure. That
rotational procedure maintains orthogonal factors of learning style
variables and maximizes the eigenvalues for each of the groupings of
majors. The eigenvalues that emerged from this process are reported in
Table 2 for each of the discipline groupings. The results reported for
each factor include the correlated learning style preferences, the
category of the preferences, the factor loadings (the standardized
correlation coefficient between the factor and preference), and the
indication of the aforementioned results in terms of the learning style
model.
The results are reported in Tables 3, 4, 5 and 6. It should be
noted on the basis of the principal components analysis procedure (i.e.,
before the Varimax rotation), the eigenvalues of the fifth factor for
each of the groupings of discipline majors was less than 1.3. For many
applications of factor analysis, a threshold of 1.3 for the eigenvalues
would be considered reasonable. In this case, the four factors that were
created were sufficient to answer the fundamental question posed by this
research--the extent to which there were meaningful differences in
learning style profiles among the three discipline groupings.
The results for the first factor are reported in Table 3. Five of
the six preferences with which the first factor correlated for the
science major constitute the entire perceptual learning style category.
This result indicated that the perceptual category aligns more
importantly with the cohort of students majoring in science than with
the business and social science majors. In this way, the results for the
first factor differentiated the science majors from both the business
and social science majors.
The first factor correlated with the preferences for light, sound
and persistence in such a way that indicated both the business and
social science majors were characterized as global learners, a component
of the psychological category. This indication is confirmed by the
positive correlation of the first factor with the global/analytic
preference for the business and social science majors. In addition, six
of the seven preferences with which the first factor correlated for the
business majors, also correlated with the first factor for the social
science major. The factor loadings of the six common preferences were
similar in sign and magnitude. Consequently, the business majors were
not differentiated from the social science majors based on the first
extracted factor.
The results for the second extracted factor are reported in Table
4. The second factor correlated negatively with the preference for
light, positively with the preference for sound, both components of the
environmental category, and negatively with the persistence preference,
a component of the emotional category, for the science majors. These
results indicated that the science majors, like the business and social
science majors, were characterized by a global learning style, a
component of the psychological category. This indication is confirmed by
the positive correlation of the second factor with the global/analytic
preference for the science majors. None of the preferences with which
the second factor correlated for the science majors were common to the
business or social science majors.
The positive correlation between the second factor and the morning
time-of-day preference for the business major indicated that the
business majors prefer to learn in the morning. This result
differentiates the business major form both the science and social
science majors.
The preferences that correlated with the second factor for the
business major included all of the preferences that correlated with the
second factor for the social science major. Although these results
differentiated the science major from the business and social science
majors, they did not differentiate the business major from the social
science major.
The results for the third factor, reported in Table 5, distinguish
the learning style of social science majors from both the business and
science majors in two ways. First, the third factor for the social
science major correlated positively with the evening time-of-day
preference, a component of the physiological category, but correlated
negatively for both the business and science majors. This result
indicates that the social science majors prefer to learn in the evening
as opposed to the business and science majors who do not. Note that the
negative correlation between the third factor and both the morning
time-of-day preference and evening time-of-day preference for the
science major indicated that the science majors prefer to learn in the
afternoon. The negative correlation between the third factor and the
evening time-of-day preference for both the business and science majors
is consistent with the finding in Table 4 that business majors prefer to
learn in the morning.
Second, the third factor correlated positively with the motivation
preference, a component of the emotional category, and indicated that
that the social science majors are externally motivated. In contrast,
the second factor correlated negatively with the motivation preference
for the business major and negatively with the first factor for science
major, indicating that the business and science majors both were
internally or self-motivated.
The results for the fourth factor, reported in Table 6, also
distinguished the social science major form the business and science
majors. The positive correlation between the fourth factor and the
preference for structure, a component of the emotional category,
indicated that the social science majors prefer more externally imposed
structure in the learning process. However, the first factor and the
second factor correlated negatively with the preference for structure
for the business and the science majors, respectively, which indicated
that both the business and science majors preferred internally imposed
structure in the learning process, contrary to the social science
majors.
The fourth factor correlated negatively with the seating
preference, a component of the environmental category, for both the
business and science majors. But the third factor correlated positively
with the seating preference for the social science major. These results
indicated that the business and science majors preferred an informal
seating arrangement, like sitting in a comfortable chair, when learning,
as opposed to the social science majors who preferred a more formal
seating arrangement, e.g., sitting at a desk.
Finally, the fourth factor correlated positively with the internal
kinesthetic preference, a component of the perceptual category, for the
business and science majors alike, which indicated a proclivity to read
aloud to internalize and learn new information. However, the first
factor correlated negatively with the internal kinesthetic preference
for social science major, contrary to the business and science majors.
DISCUSSION
The U.S. higher education system globally ranks fourteenth in the
percentage of twenty-five to thirty-four year olds with a college degree
(OECD, 2012). One reason the U.S. is ranked comparatively low is because
the educational system "is not especially efficient,"
(Colander, 2013, p.635), partly due to the ineffectiveness of both
university and high school counselors to adequately advise students when
selecting their academic majors (Crampton, Walstrom & Schambach,
2006). If students with a certain unique, specialized learning style
were advised to select a major that other students with a similar
learning style historically had been successful, then student
achievement, the effectiveness of the academic advisement process and
the efficiency of the U.S. higher education system likely would
increase. Unfortunately, information regarding which learning styles
align favorably with different college majors is virtually nonexistent.
This study examined the learning styles of over three thousand
incoming college freshmen majoring in business, science and social
science to identify the learning styles that aligned with the
alternative major categories. The twenty-six preferences that compose
the six categories of the Dunn and Dunn (Rundle & Dunn, 2009)
learning style model were identified via the BE survey (Rundle &
Dunn, 2009a) and matched with the freshmen's selected majors.
Pair-wise t-tests established that there were significant mean
differences for over seventy percent of the learning style preferences
between the cohorts of students majoring in business, science and social
science.
Factor analysis was utilized to differentiate the learning styles
of the social science majors from the business and science majors in
several important ways. First, the social science majors preferred an
externally imposed learning structure, a component of the emotional
category, as opposed to both the business and science majors who
preferred an internally imposed learning structure, or the opportunity
to learn the material in their own way. Second, the social science
majors were externally motivated to learn, another component of the
emotional category, contrary to the business and science majors who both
were internally motivated to learn. Third, the social science majors
preferred a formal seating arrangement, such as sitting at a desk in the
library, a component of the environmental category. Both business and
science majors preferred an informal seating arrangement such as sitting
on a couch or lying on a bed when learning. Fourth, the social science
majors had a proclivity for verbal kinesthetic learning, a component of
the perceptual category, which indicated that the social science majors
preferred to read aloud to internalize and learn new information. In
contrast, the business and science majors evinced a negative proclivity
for the verbal kinesthetic learning modality. Finally, the social
science majors' preferred time-of-day to learn was in the evening,
a component of the physiological category.
The science majors' preferred time-of-day to learn was in the
afternoon, and the business majors' preferred time-of-day to learn
was in the morning. These differences in the time-of-day component of
the physiological category differentiated the cohorts of students in all
three major categories. In addition, the science majors were
differentiated from the business and social science majors with regard
to the preferences for temperature when learning, an environmental
category. Science majors preferred a warm learning environment and the
business and social science majors preferred a cool learning
environment.
In addition to identifying the differences in the learning styles
of the cohorts of students who majored in business, science and social
science, the factor analysis also revealed an important commonality
among the alternative academic majors: the business, science and social
science majors all were characterized by the global learning style. This
commonality among the alternative majors indicated that the majority of
students in the sample processed information deductively--reasoning from
a general conclusion to specific facts.
These results also lend support to the contentious view that
developing a better understanding of student learning styles may be
instrumental in improving cognitive outcomes. Prior research (e.g.,
Terregrossa et al., 2012) has focused on the potential value of
determining the learning styles of students in a given class and
tailoring the manner of teaching in that class in order to achieve
greater congruency between those teaching methods and the students'
learning style preferences. The present paper has implications relevant
to the hypothesis that students' choice of majors may have an
impact on the cognitive performance of those students to the extent that
the core substantive material within a given academic discipline may be
more or less congruent with a given learning style preference pattern.
In such a case, a recognition of what types of learning style profiles
are more conducive to better achievement in a given discipline would
potentially allow many students to reach higher levels of academic
achievement as well as place such students along a more productive
career path. The preliminary conclusion of the present paper is that the
findings are consistent with this hypothesis. However, as indicated
above, additional analysis integrating the pattern of learning style
profiles of students in different majors with various academic
performance metrics needs to be undertaken before this additional and
heretofore untested hypothesis can be assessed.
DIRECTIONS FOR FUTURE RESEARCH
The research results reported here suggest that such a research
agenda may well prove to be an important component in a process that
would allow university counseling and student support staff to be much
more helpful in providing evidence-based advise to students to
facilitate the university's goal of greater student retention and
the students' goal of a program of study that is more compatible
with the student's academic abilities and interests and with the
students' career success. One might even imagine that such an
increase in compatibility might lead to students having a greater
appreciation of the college learning process and a greater motivation to
perform well in course work.
Of course, more work needs to be done. A reasonable next step would
be to perform similar paired t-test analysis and factor analysis for
students' choice of majors within the broad categories considered
here. For example, given that the research results reported here
indicate significant differentiation between the learning style profiles
of students choosing to specialize in the business disciplines, the
science disciplines and the social science disciplines, might the same
degree of differentiation be observed in learning style profiles among
those business majors who choose to major in accounting versus
management, or marketing versus finance, or operations management versus
economics, etc.? Again, that step would perhaps further solidify the
groundwork leading to an integration of the learning style profile data
and choice of major data with student follow-up data representing
performance related outcomes such as student retention, time to
graduation, student grade performance, satisfactory job placement and
earnings which could potentially be used by counselors to help students
gain a better understanding of their own skill sets and interests,
leading to more productive and efficient decisions in the near term and
long term.
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Landreth, H.; & Colander, D. (2002). History of economic
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Lewin, T. (2013). Interest fading in humanities: Colleges worry.
New York Times, October 31, A1.
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Rundle, S., & Dunn, R. (1996-2009a). Building Excellence[R]
(BE) Survey. www.learningstyles. net.
Rundle, S., & Dunn, R. (1996-2009b). Building Excellence (BE)
Survey 2000 Research Manual.
http://www.asb.dk/fileexplorer/fetchfile.aspx?file=7783.
Simons, K.A., Lowe, D.R.; & Stout, D.E. (2003). Factors
influencing choice of accounting as a major. Proceedings for the 2003
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Smith, A. (1937). An inquiry into the nature and causes of the
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Terregrossa, R., Englander, F., Wang, Z.; & Wielkopolski, T.
(2012). How college instructors can enhance student achievement: Testing
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International Journal of Education Research, 7(1), 1-15.
Ralph A. Terregrossa
St. John's University
Fred Englander
Zhaobo Wang
Fairleigh Dickinson University
Ralph A. Terregrossa is an Associate Professor of Economics at St.
John's University in New York City. He holds a Ph.D. in economics
from Binghamton University. Dr. Terregrossa has published articles in
The Quarterly Review of Economics and Finance, International Advances in
Economic Research and Educational Review.
Fred Englander is a Professor of Economics at Fairleigh Dickinson
University in Madison, New Jersey. He received his Ph.D. in economics
from Rutgers University. Dr. Englander has published articles in the
Southern Economics Journal, Business Ethics Quarterly, Journal of
Academic Ethics and the Journal of Education for Business.
Zhaobo Wang is an Associate Professor of Production and Operations
Management at Fairleigh Dickinson University, Madison, New Jersey. Dr.
Wang received a Ph.D. in operations research from Rutgers University. He
has published articles in the Journal of Educational and Behavioral
Statistics and the Journal for Economic Educators.
Table 1
Results of Pair-Wise t-test for Differences in Means
Learning Style Business vs. Science vs.
Preferences Science Social Science
t-value p-value t-value p-value
auditory -2.082 0.037 * 0.433 0.665
visual picture -1.356 0.175 -0.138 0.890
visual word -4.728 0.000 ** 0.134 0.894
tactile/kinesthetic -0.802 0.423 -4.031 0.000 **
internal kinesthetic 0.281 0.779 -1.493 0.136
global/analytic 3.438 0.001 ** 0.757 ** 0.449
impulsive/reflective 6.028 0.000 ** -2.588 0.010 *
warm 2.022 0.043 * 0.400 0.689
informal 0.925 0.355 2.959 0.003 **
light -3.224 0.001 ** 3.800 ** 0.000 **
sound 6.166 0.000 ** -6.908 0.000 **
intake 2.443 0.015 * -3.284 0.001 **
mobile 1.421 ** 0335 ** -0.173 0.862
morning -2.404 0.016 * 3.222 0.001 **
late morning/early p.m. 0.281 0.779 -0.950 0.342
late afternoon 0.610 0.542 -4.275 0.000 **
evening -1.050 0.294 -2.428 0.015 *
persistence -1.857 0.063 0.277 0.782
motivation 4.138 0.000 ** 2.776 0.006 **
conformity -0.810 0.418 3.663 0.000 **
structure -3.322 0.001 ** 2.046 0.041 *
alone/pair -3.599 0.000 ** -0.306 0.760
small group -1.080 0.280 1.977 0.048 *
authority 1.568 0.117 2.439 0.015 *
variety 2.082 0.037 -1.144 0.253
team 0.378 0.705 0.714 0.475
Learning Style Social Science
Preferences vs. Business
t-value p-value
auditory 1.450 0.147
visual picture 1.313 0.186
visual word 4.177 0.000 **
tactile/kinesthetic 4.326 0.000 **
internal kinesthetic 1.067 0.286
global/analytic -3.686 0.000 **
impulsive/reflective -3.140 0.002 **
warm -2.179 0.029 *
informal -3.496 0.000 **
light -0.605 0.545
sound 0.692 0.489
intake 0.831 0.406
mobile -1.118 0.264
morning -0.780 0.435
late morning/early p.m. 0.609 0.543
late afternoon 3.270 0.001 **
evening 3.154 0.002 **
persistence 1.397 0.163
motivation -6.120 0.000 **
conformity -2.512 0.012 *
structure 1.048 0.295
alone/pair 3.446 0.001 **
small group -0.826 0.409
authority -3.448 0.001 **
variety -0.801 0.423
team -0.967 0.334
* significant at .05 level, ** significant at .01 level
Table 2
Eigenvalues and Variance Explained by Each Factor
Based on Principal Component and Varimax Rotation
Business Science
Factor Eigenvalue % explained Eigenvalue % explained
F1 2.5638 11.15% 2.5974 11.29%
F2 2.5143 10.93% 2.4433 10.62%
F3 1.9341 8.41% 1.8323 7.97%
F4 1.9129 8.32% 1.7843 7.76%
Total 8.9251 38.80% 8.6574 37.64%
Social Science
Factor Eigenvalue % explained
F1 2.2510 9.79%
F2 2.1873 9.51%
F3 2.0745 9.02%
F4 1.8586 8.08%
Total 8.3715 36.40%
Table 3
Learning Style Characteristics of the First Extracted
Factor for Business, Science and Social Science Majors
BUSINESS MAJORS
Preference Category Factor Loading
light environment -.290
global/analytic psychological .650
impulsive/reflective psychological .519
mobility physiological .507
persistence emotional -.706
sound environmental .611
structure emotional -.575
SCIENCE MAJORS
Preference Category Factor Loading
auditory perceptual .543
internal kinesthetic perceptual .700
motivation emotional -.581
tactile/kinesthetic perceptual .709
visual picture perceptual .575
visual word perceptual .501
SOCIAL SCIENCE MAJORS
Preference Category Factor Loading
light environment -.356
global/analytic psychological .689
impulsive/reflective psychological .469
internal kinesthetic perceptual -.043
intake physiological .342
mobility physiological .419
persistence emotional -.608
sound environmental .503
BUSINESS MAJORS
Preference Indication
light prefers low light
global/analytic global learner
impulsive/reflective impulsive in nature
mobility prefers mobility when leaning
persistence simultaneously works on several tasks
sound prefers background sound
structure prefers less structure
SCIENCE MAJORS
Preference Indication
auditory prefers lectures
internal kinesthetic prefers reading aloud
motivation internally motivated
tactile/kinesthetic prefers learning by doing
visual picture prefers pictures, graphs, diagrams
visual word prefers reading text
SOCIAL SCIENCE MAJORS
Preference Indication
light prefers low light
global/analytic global learner
impulsive/reflective impulsive in nature
internal kinesthetic does not prefer reading aloud
intake prefers snacks/drinks
mobility prefers mobility when learning
persistence simultaneously works on several tasks
sound prefers background sound
Table 4
Learning Style Characteristics of the Second Extracted
Factor for Business, Science and Social Science Majors
BUSINESS MAJORS
Preference Category Factor Loading
auditory perceptual .462
internal kinesthetic perceptual .681
morning physiological .271
motivation emotional -.639
tactile/kinesthetic perceptual .643
visual picture perceptual .594
visual word perceptual .507
SCIENCE MAJORS
Preference Category Factor Loading
light environment -.374
global/analytic psychological .668
impulsive/reflective psychological .559
persistence emotional -.706
sound environmental .518
structure emotional -.564
temperature environmental .091
SOCIAL SCIENCE MAJORS
Preference Category Factor Loading
auditory perceptual .311
tactile/kinesthetic perceptual .757
visual picture perceptual .645
visual word perceptual .361
BUSINESS MAJORS
Preference Indication
auditory prefers lectures
internal kinesthetic prefers reading aloud
morning prefers to learn in the morning
motivation internally motivated
tactile/kinesthetic prefers learning by doing
visual picture prefers pictures, graphs and diagrams
visual word prefers reading text
SCIENCE MAJORS
Preference Indication
light prefers low light
global/analytic global learner
impulsive/reflective impulsive in nature
persistence simultaneously works on several tasks
sound prefers background sound
structure prefers less structure
temperature prefers cool environment
SOCIAL SCIENCE MAJORS
Preference Indication
auditory prefers lectures
tactile/kinesthetic prefers learning by doing
visual picture prefers pictures, graphs and diagrams
visual word prefers reading text
Table 5
Learning Style Characteristics of the Third Extracted
Factor for Business, Science and Social Science Majors
BUSINESS MAJORS
Preference Category Factor Loading
authority sociological .672
conformity emotional .575
evening physiological -.477
variety sociological .678
temperature environmental -.148
SCIENCE MAJORS
Preference Category Factor Loading
authority sociological .690
conformity emotional .560
evening physiological -.356
morning physiological -.255
variety sociological .653
SOCIAL SCIENCE MAJORS
Preference Category Factor Loading
authority sociological .673
conformity emotional .608
evening physiological .438
seating environmental .274
motivation emotional .485
variety sociological .566
BUSINESS MAJORS
Preference Indication
authority prefers authoritative teacher
conformity conformist
evening doesn't prefer to learn in the evening
variety prefers variety in instruction methods
temperature prefers warm environment
SCIENCE MAJORS
Preference Indication
authority prefers authoritative teacher
conformity conformist
evening doesn't prefer to learn in the evening
morning prefer to learn in the morning
variety prefers variety in instruction methods
SOCIAL SCIENCE MAJORS
Preference Indication
authority prefers authoritative teacher
conformity conformist
evening prefers to learn in the evening
seating prefers formal seating
motivation externally motivated
variety prefers variety in instruction methods
Table 6
Learning Style Characteristics of the Fourth
Extracted Factor for Business, Science and Social
Science Majors
BUSINESS MAJORS
Preference Category Factor Loading
seating environmental -.423
internal kinesthetic perceptual .382
alone sociological .600
team sociological .723
SCIENCE MAJORS
Preference Category Factor Loading
seating environment -.456
internal kinesthetic perceptual .456
mobility physiological .393
alone sociological .515
team sociological .622
SOCIAL SCIENCE MAJORS
Preference Category Factor Loading
morning physiological .257
alone sociological .732
structure emotional ..475
team sociological .778
temperature environmental .235
BUSINESS MAJORS
Preference Indication
seating Prefers informal seating
internal kinesthetic Prefers reading aloud
alone Prefers to learn alone
team Prefers to learn with a team
SCIENCE MAJORS
Preference Indication
seating Prefers informal seating
internal kinesthetic Prefers reading aloud
mobility Prefers mobility
alone Prefers to learn alone
team Prefers to learn with a team
SOCIAL SCIENCE MAJORS
Preference Indication
morning Prefers early morning
alone Prefers to lean alone
structure Prefers structure
team Prefers to learn with a team
temperature Prefers cool temperature