Learning style theory as a potential tool in guiding student choice of college major.
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?
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.
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.
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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International Journal of Education Research, 7(1), 1-15.
Ralph A. Terregrossa
St. John's University
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
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|Author:||Terregrossa, Ralph A.; Englander, Fred; Wang, Zhaobo|
|Publication:||International Journal of Education Research (IJER)|
|Date:||Mar 22, 2015|
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