Latest articles from "College Student Journal":








Other interesting articles:

Linguistically Appropriate Practice: A Guide for Working with Young Immigrant Children
Canadian Journal of Education (October 1, 2014)

Subverting the Rhetoric of Assimilation: Ella Cara Deloria (Dakota) in the 1920s
Hecate (January 1, 2013)

Birch necrotic leaf spots caused by fungal pathogens
Folia Oecologica (July 1, 2014)

How do Master Athletes Cope with Pre-Competitive Stress at a "Senior Games"?
Journal of Sport Behavior (June 1, 2012)

Coping strategies associated with participation and quality of life in older adults/Stratégies d'adaptation associées à la participation et la qualité de vie chez les personnes âgées
The Canadian Journal of Occupational Therapy (February 1, 2015)

Linguistic Lingo And Lyric Diction VI-Assimilation
Journal of Singing (May 1, 2015)

Fast Horses and Strong Women: Revisioning the Sport-Family-Business of Harness Horse Racing
Sociological Viewpoints (October 1, 2011)

Publication: College Student Journal
Date published:
Language: English
PMID: 24083
ISSN: 01463934
Journal code: CLSJ


Families, students, and institutions invest enormous financial resources in higher education. Moreover, this investment is not solely financial; students also try to build up their adult life and gain effective equipments in the higher education process. Motivation to protect this investment and the rates of failing in university and dropping out from graduate school force researchers to understand the variables related to academic success and failure, and share this knowledge with all stakeholders, namely families, students, universities, as well as policy makers.

Educational achievement depends not only on the intellectual ability and skills of the learner, but also on the individual's learning style (KoIb, 1984), which refers to the consistent way in which a learner responds to or interacts with stimuli in the learning context, as a replacement of cognitive styles theorems from 1970s (Loo, 2004). Learning styles are defined as different ways used by individuals to process and organize information and as a sort of way of thinking, comprehending and processing information (KoIb, 1984; Sadler-Smith, 1996). In this sense, learning style is related to both sensory and the mental. Kolb's Learning Cycle and Learning Style Inventory (KoIb, 1984) are widely used in order to understand the stages of learning and the ways people prefer to receive and process new information.

In the literature, study skills refer to the student's knowledge of study methods, the ability to manage time and available resources to meet the demands of academic tasks. Study habits reflect regular acts of studying in which the students engage in. Study attitudes are defined as attitudes toward studying, and students' approval of the broader goals of higher education (Crede & Kuncel, 2008). Many researchers are interested in identifying these variables that contribute to the performance of a university student's academic success. Some empirical findings, on the other hand, have demonstrated that a significant number of undergraduates possess inadequate study skills, such as difficulties with time management, note-taking, understanding how to prepare for different types of tests, and managing anxiety. Fortunately, examples of qualities or behaviors such as student motivation, learning habits, study skills and beliefs about success, may be enhanced via external instructions and support (Proctor, Prevatt, Adams, Reaser, & Petscher, 2006). As can be seen, university students' approaches to learning vary and their ways of learning may not always match those that professors use in their teaching. Which learning styles do students prefer? How do effective learning habits relate to their individual characteristics and skills, like time management and studying? The current study explores learning styles and effective learning habits in a Turkish university.

Learning styles, learning skills, and learning habits

Application of learning style theories is a popular strategy for improving the education of university students and is consistent with responding to individual educational needs (Salter at al., 2006). Various theories were developed in order to describe the observed differences of approaches to learning, such as Gardner's multiple intelligences (1993), Kolb's learning styles (1985), Gregorc's cognitive style differences (1984), and various extensions of Jung's psychological types (1970). There is empirical and theoretical literature on study skills, study habits, study attitudes, meta-cognitive skills (Crede & Kuncel, 2008), learning styles and learning preferences (KoIb, 1985). Frequently used inventories include the Survey of Study Habits and Attitudes (Brown & Holtzman, 1967), Learning and Study Skills Inventory (Weinstein & Palmer, 2002), Inventory of Learning Processes (Schmeck, Geisler-Brenstein, & Cercy, 1991), the Study Process Questionnaire (Biggs, 1987), the Learning Style Inventory (KoIb 1985), and other learning style inventories (e.g., Dunn & Dunn, 1978; Felder & Silverman, 1988; Jung, 1970; KoIb, 1985; McCarthy, 1987).

Experiential Learning Theory (ELT) represents the work of twentieth century theorists of human learning and development-notably John Dewey, Kurt Lewin, Jean Piaget, William James, Carl Jung, Paulo Freiré, and Carl Rogers- who placed conscious intentional action based on subjective experience at the center of the learning process (KoIb & KoIb, 2005). The theory (KoIb, 1984) defends that learning is a combination of experience, cognition, perception, and behavior. Based on ELT, David A. KoIb (1984) developed Learning Cycle Model and Learning Style Inventory in 1969. Kolb's Learning Cycle model presents a meta-cognitive experiential learning process based on adult learning and group dynamics. Kolb's Learning Cycle model is a popular approach to defining and measuring learning styles in higher education (Salter et al., 2006). According to Kolb's Learning Cycle model, learning is perceived as a four-stage cycle including Concrete Experience (CE), Reflective Observation (RO), Abstract Conceptualization (AC), and Active Experimentation (AE) (KoIb, 1984). The model starts with concrete experience, which forms the basis for observation and reflection on experiences (reflective observation); these observations are assimilated into concepts and generalizations (abstract conceptualization) that guide new experiences and interactions with the world (active experimentation) (Loo, 2004). Kolb's Learning Cycle model reflects two independent dimensions based on (a) perceiving, how one makes information meaningful, which involves concrete experience (feeling) and abstract conceptualization (thinking), and (b) processing, how one takes in information, which involves active experimentation (doing) and reflective observation (watching). These two dimensions form the following four quadrants reflecting four learning styles (see Figure 1): accommodator, diverger, assimilator, and converger. Learners may prefer one component of each of these two dimensions, resulting in a preferred learning style. Preference for CE and RO results in a divergent style (diverger); RO and AC results in an assimilative style (assimilator); AC and AE results in a convergent style (converger); and AE and CE results in an accommodative style (accommodator). The model describes divergere as people who tend to be imaginative, emotional, people-oriented, and good in situations that call for generation of ideas such as brainstorming; assimilators value the world of ideas and their greatest strength lies in ability to induce reasoning, integrate ideas and formulate theoretical models; convergers, the opposite in many ways from divergere, are drawn to technical tasks and problems and tend to be good at decision making and problem solving, especially when there is a single correct answer or solution; accommodators, opposite in many ways from assimilators, are action-oriented, risk-takers, comfortable with people, and excel at carrying out plans and completing tasks (Salter et al., 2006).

According to KoIb and KoIb (2005: 8), main role of Learning Styles Inventory (LSI) is 'to serve as an educational tool to increase individuals' understanding of the process of learning from experience and their unique individual approach to learning. Thus, learners can monitor their learning process and select approaches that work best for them in different learning situations (KoIb & KoIb, 2005).

In literature, study or learning skills are defined as competence in acquiring, recording, organizing, synthesizing, remembering, and using information and ideas, and are among the skills that can be modified for learners of all ages (Proctor et al., 2006). As mentioned before, study skills include a variety of activities, including time management, setting appropriate goals, selecting an appropriate study environment, employing appropriate note-taking strategies, selecting main ideas, and organization (Proctor et al., 2006). Numerous studies have demonstrated the link between study skills and academic success (e.g., Grade Point Average) in university students (Kern at al., 1998; Proctor et al., 2006). For instance, time management with study and social activity planning has emerged as a predictor of college GPA and quality of academic performance (Britton & Tesser, 1991; Proctor et al., 2006). Also, a significant relationship was found between academic success and students' information processing skills, ability to select main ideas, self-testing, motivation, time management, amount of note taking and quality of students' notes and concentration (Baker & Lombardi, 1985; Proctor et al., 2006). Overall, it has been suggested that these and other types of study skills account for approximately 15% of the variance in the academic achievement of undergraduates (Proctor et al., 2006).


Research questions

The presented study mainly investigates effective learning habits and learning styles of undergraduate students in a Turkish university. This study aims to explore the relationships between students' learning styles, their effective learning habits, their academic performance, and their skills and demographic characteristics such as weekly time management, study planning, gender, grade level, faculty and living arrangements. The study addresses the following questions:

1 . Which learning styles do students present?

2. How do students' effective learning habits relate to their demographic characteristics such as gender, faculty, grade level and skills such as weekly time management, and study planning?

3. How do students' effective learning habits relate to their academic performance?

4. Is there a linkage between learning styles and effective learning habits?

Participants and procedure

Participants were the undergraduate students of a small private university in stanbul, Turkey. The survey instrument, the aim of the research and the consent form were mentioned to undergraduate students via e-mail and also by means of students who took the course Project 102 in the 2009-2010 Spring semester. 512 volunteer students participated in this study. Each application lasted approximately half an hour. From the total of 512 volunteer participants, 94 students did not respond to all items in the survey. Therefore, only the responses of 418 students were analyzed. There were three sections in the instrument: background information, learning style inventory (KoIb 1985), and learning habits. Excel and SPSS were used during analyses of the data.


Demographic data. Demographic characteristics and academic performance information were obtained through the individual items reflecting gender, living arrangement, grade level, faculty and cumulative grade point averages. Cumulative GPA served as a measure of academic performance.

Weekly time management and study planning. Three items were designed to assess students' weekly time management (time spent for study and time spent for social activities) and their preferences about study planning. In order to assess weekly time management, students were instructed to indicate how much time they spent in a week for studying and for social activities ranging from 1-5 hours, 6-10 hours to 1 1 hours and plus. With regard to study planning, students were asked to determine whether they generally study regularly, during exam weeks, or just before the day of exam.

Learning styles. The Learning Styles Inventory (LSI-2) (KoIb, 1985) was used to examine students' individual learning preferences. The LSI is a self-report instrument designed to examine individual's preference for learning along the four dimensions of experiential learning theory (KoIb, 1985). KoIb (1985) developed the LSI based on his theory of experiential learning on peoples' different approaches to perceiving and processing information. The LSI is a self-report instrument and is composed of 12 short statements followed by four possible sentence endings. The individuals are required to rank order each of four sentence endings based on their preference for using the four distinct learning modes. Every individual utilizes each of the four learning modes to some extent, but she/he also has a preferred learning style for perceiving and transforming the information. Turkish version of The LSI-2 was adapted by Askar and Akkoyunlu (1993). Turkish version is a reliable and valid instrument; internal reliability of four main learning modes and two bi-polar dimensions were found to be high with a Cronbach alpha between .88 and .73 (see Askar & Akkoyunlu, 1993). For the current study, it was observed that the internal consistencies of four basic learning modes were between .73 and .84.

Effective learning habits. In the literature, there are numerous inventories which tend to focus on measurement of study skills, habits and attitudes. Some of them are based on empirical approach and include items that especially distinguish between over and underachievers. Some others are based on theoretical considerations or on qualitative analyses of the articulated ways used by students while learning (Crede & Kuncel, 2008). The researchers of the current study preferred to develop a measurement tool derived from the verbalized learning habits of the Turkish students rather than adapt an inventory which was elaborated in a different culture. The lead author, a specialist of the Academic Support Program at Sabanci University, generated 18 items based on non-structured interviews conducted by the peer moderators and students who took her course (Project 102) in 2008-2009 Spring Semester. Peer assistants, as members of the Academic Support Program, coordinate and execute active learning and peer study and discussion sessions, and they are supervised by program specialists with regular meetings. These meetings and discussion sessions served as fruitful environment to examine common learning habits used by students. Peer assistants and students were asked to simply verbalize about their useful learning habits, such as how they learn effectively and which useful strategies they engage in. The statements that were most commonly used by students were selected. Some of the statements reflected overt behaviors such as generating questions about reading materials, asking for help from peers and instructors, doing library research; some of them referred cognitive efforts like trying to build an interconnection between different courses, using information to solve practical problems and the like. The 18 items and a demographic form were administered to 1 1 1 undergraduate students and they were instructed to indicate how frequently they used each learning habit on a scale ranging from 1 (never) to 5 (always). A total of 68 men and 43 women participated in the pilot study in 2008-2009 Spring Semester. Table 1 shows the Effective learning Habits items' means, standard deviations, and item-total correlation. Considering reliability, the internal consistency of the total scale was = .83. Deletion of individual items did not result in a drop in alpha below .81, indicating that all items contribute relatively equally to the consistency of the scale. In order to examine possible factor structure of the scale, a principal component analysis with varimax rotation with an eigenvalue 1.00 as the criterion, was conducted with no limitation on numbers. In this analysis, most items loaded highest on one general factor. A similar pattern was also observed according to scree plot test and it was concluded that the scale is unidimensional in nature. According to the results of the pilot study, it was thought that the scale is a psychometrically sound and a reliable measure, and a total score served as an index of learning habits in the study which was conducted in 2009-2010 Spring Semester. For this study, Cronbach alpha was found to be .83, as in the pilot study (see Table 1).


Students' characteristics, weekly time management and study planning

Forty-three per cent of the participants were (n = 181) female and 57% were male (n = 237). A large majority of the students were living in dorms (n = 345). The survey was administrated to students from two different faculties: (1) Faculty of Engineering and Natural Sciences (FENS); (2) Faculty of Arts and Social Sciences (FASS), and Faculty of Management (FMAN). Sixty two per cent of the students (n = 260) were participated from FENS and 37.8% (n = 158) were participated from FASS and FMAN. Students from FENS were overrepresented, since they form the majority of university population. With regard to academic performance, 17.4% (n=73) students had a cumulative GPA between 1-2, 45% (n=l 88) had a cumulative GPA within the range of 2-3, and 37.6% (n=157) had a cumulative GPA between 3-4. Academic performance was classified into three groups, namely; pass, successful and very successful. The percentages for time spent for studying and engaging in social activities were relatively similarly distributed among the students. Most students prefer to study for exams, in other words, during mid-term weeks; only a small percentage (12%) is prone to studying regularly. Other features of the sample are described in Table 2.

Learning styles

Knowledge of individual learning styles can help instructors and advisors to design a learning environment that can be suited to students with different interests and preferences. With this in mind, an attempt was made to describe the learning styles of students according to gender, grade level, faculty, cumulative GPA, weekly time management and study planning. Kolb's Learning Styles distributions are shown in Table 3. As can be seen from the table, there were more students with a converging preference (60.5%). The second most prevalent style was assimilating (30.1%); distributions of diverging (4.55%) and accommodating (4.78%) styles, on the other hand, were considerably similar to each other for all participants. Moreover, this pattern was observed across all variables. As a result, it can be proposed that Turkish students generally like to learn through practical application like solving problems, trying to make correct decisions and preferring to deal with technical works or problems as opposed to working with social relations (converging). Second dominant learning style was focusing on abstract concepts, making reflective observation and assimilating them into an integrated explanation (assimilating). Turkish students rarely prefer learning through carrying out experiments, taking risks (accommodators); generating new ideas, observing situations from different perspectives, and bringing different ideas together (divergers).

Since evaluating individual differences in learning styles was a core objective of the current study, we compared percentages of converger, assimilator, accommodator and diverger styles according to school, gender and academic performance. A series of two-sample t-test were performed to determine whether there were significant differences between groups in terms of proportions of four learning styles (see Table 4). When we looked at faculties of Sabanci University, the results showed that there were significantly greater numbers of students in the converger style among the social science students (FASS and FMAN) (68.99%) compared to engineering students (FENS) (55.33%). For the assimilator style, the opposite pattern was observed in that there were significantly more students in FENS (35%) compared to FASS and FMAN (22.15%). For accommodator and diverger styles, the proportions were found to be similar to each other across the faculties. Women and men, on the other hand, did not show any preference with respect to learning style.

When academic performance is considered, students who had a cumulative GPA between 3-4 revealed a clear preference for converger style (71.34%) compared to students who had a cumulative GPA between 2-3 (53.19%), and to students with a cumulative GPA between 1-2 (56.16%). Conversely, students who had a cumulative GPA between 2-3 (37.16%) and students who had a cumulative GPA between 1-2 (36.99%) reported predominantly assimilator learning style compared to the very successful group (17.83%) (see Table 5).

Effective learning habits and academic achievement

From an empirical approach, an instrument is expected to distinguish among different levels of academic achievement. At a practical level, identifying variables that are most strongly related to academic performance may enable educators as well as students to improve learning practices. One-way ANOVA was performed, comparing the three cumulative GPA groups as the independent variable and total score of effective learning habits as dependent variable, in order to find out whether varying degrees of effective learning habits related to different levels of academic performance. The ANOVA for academic performance and effective learning habit was significant, F(2, 417) = 33.5, p < .001. Post hoc comparisons (Bonferroni) were conducted to find out the source of difference, and the results revealed that students with high academic performance significantly reported higher levels of effective learning habits (M = 59.47, SD = 11.78) than those with low cumulative GPA's (pass group, M = 48.34, SD = 1 1 .78; successful group, M = 54.69, SD = 9.50) (see Table 6). These results also supported the criterion-related validity of instrument, because the instrument was supposed to measure effective learning habits, and students were asked about their useful learning habits during the item development process.

Although this research study was primarily interested in the interplay between academic performance and using effective learning habits, it also examined other demographic variables in an attempt to understand individual differences in using effective learning habits. An independent t-test was conducted to determine possible gender differences. It was found out that women's mean (M = 57.66, SD = 9.32) effective learning habits score was significantly higher than men's mean score (M = 53.62, SD = 10.94, i(41 1) = 4.06, p < .001). One way ANOVAs were performed to compare the three faculties and four grade levels. Although the ANOVA for faculty was not significant F(2, 417) = .66, p>.05, comparisons of the four grades on effective learning habits levels yielded significant differences F(2, 417) = 7.18, p < .001. Post hoc comparisons (Bonferroni) yielded that senior students reported significantly greater use of effective learning habits (M = 58.75, SD = 10.88) than freshmen students (M = 52, SD = 10. 10). On the other hand, mean scores of sophomore (M = 55.14, SD = 9.55) and junior students (M = 55.40, SD = 9.96) were found to be similar (see Table 7). According these results it can be proposed that students "leam" to use effective learning habits progressively.

Similarly, one way ANOVAs were conducted to compare three levels of weekly time management and study planning. The analyses yielded significant differences for all variables. Firstly, it was found that students who spent 6-10 hours for study in a week reported significantly higher level of effective learning habits than those who spent less time studying F(2, 417) = 33.03. p < .001 . Similar results were yielded for weekly social activities F(2, 417) = 3.25. p< .05, in that students who were socially more active also engaged in high levels of effective learning habits compared to students who spent less time for social activities. It is also not surprising that participants who study regularly reported significantly higher levels of effective learning habits than those who study only during exam weeks, or just before the day of exam F(2, 417) = 44.43, p < .001 (see Table 8).

The effective learner can use any of the four styles in different learning situations rather than relying only on his/her preferred style (KoIb 1985). This shows that learning styles are neither better, nor worse than each other. Nevertheless, four learning styles are compared in terms of using effective ways of learning by conducting a one way ANOVA. As expected, no significant differences were yielded F(3, 417) = .359, p > .05. In other words, using different learning styles (diverging, accommodating, converging, assimilating) did not make any contribution to the levels of using effective ways of learning.


According to Kolb's Learning Styles distributions (Kolb, 1985), there were more students with a converging preference, and the second most prevalent style was assimilating. However, there were fewer students with diverging or accommodating preferences. As the most dominant learning style of the university students at Sabanci University (SU), convergers are best at finding practical uses for ideas, they need to perceive the whole, and move from the whole to the part. As the second dominant learning style, assimilators are best at understanding a wide range of information and putting it into a concise and logical form. SU students rarely prefer learning by their feelings (accommodators and divergere) or viewing concrete situations from many different perspectives. As a result, it can be proposed that emphasizing feeling and an intuitive approach were not favored by SU students. This pattern did not show any variation according to certain crucial characteristics such as school, grade level, academic success, skills like time management and study planning. Although it is a mere speculation, it could be proposed that students, at least in SU, seemed very conservative when learning styles were at issue. At present, Turkey has 156 universities (103 public, 53 private) and since the number of students motivated to get into higher education exceeds the capacity of the university system, nationwide university placement examination is held on an annual basis. The national exam is multiple-choice in nature, and consists of verbal and quantitative aptitude tests. High school education is unfortunately devoted to prepare students for the ultimate goal, which is to be successful on this exam. Students' main endeavor is to be good at choosing the correct answer or solution to a question. Success in high school courses are generally undermined by students, families, even instructors due to the current system. Thus, students were not provided with a learning environment that facilitates creating theoretical models, assimilating models into an integrated explanation, carrying out plans and experiments, taking risks, viewing situations from many perspectives, being imaginative, emotional, and finally, being able to relate to people.

Although our aim was not to diagnose students on the basis of their learning styles, Kolb and Kolb (2005) suggest that the effective learner can use each of the learning styles in different learning situations rather than rely only on his/her preferred style. Learning is an ongoing and holistic process of adaptation to the environment; it is not only the result of cognition but also the result of the ability to move back and forth between different modes of thinking, feeling, perceiving, and behaving. In that sense, a learner is supposed to create social knowledge and recreate personal knowledge through the transformation of experience. From this perspective, a dominant learning style (converging) across all variables in higher education may be a sign of limited repertoire, and this issue needs to be considered by instructors and policy makers, at least in Sabanci University.

Other than investigating learning styles of students, this research study was interested in the interplay between academic performance and using effective learning habits, and examined other demographic variables such as grades, gender, time management skills, study and social activity planning skills in order to understand individual differences in using effective learning habits. Academic performance increased when students used effective learning habits. Students who spent time for study and social activities for about ten hours a week were found to use higher levels of effective learning habits. The findings supported that developing study skills and time management skills are key factors in using effective learning habits. It was found that women's effective learning habits score was significantly higher than men's score, but there were no significant differences between their learning styles. Study skills, time management skills and learning habits exhibited strong relations with academic performance in university. Therefore, providing workshops and advising sessions intended to help students acquire appropriate study skills and effective learning habits gain importance. In addition, recognizing the learning styles of students contributes to effectiveness (Kazu, 2009). Learning styles must be kept in mind during the education and training process, and student's interests, expectations and needs should not be ignored. We also examined possible linkage between learning styles and effective learning habits, but results recommended that there was not a significant contribution of using different learning styles to the varying level of using effective learning habits. It could be said that this finding is in line with literature (e.g. Loo, 2004) and a possible relationship between two constructs needs further investigation.

In conclusion, knowing the learning styles of students is highly important both for students and instructors. Knowledge of learning styles affects students' awareness of how they learn best and contribute to their taking responsibility of their own learning. Since instructors prefer to teach in a cognitive style that matches their own, knowing the preferred learning styles of students can help instructors to adjust their teaching methods and evaluation tools to best match the different needs of their students (Wolfe et al., 2006). Finding out students' learning styles may empower their learning experiences, which should be the ultimate goal.


Askar, P. & Akkoyunlu B. (1993). KoIb learning style inventory. Education and Science, 15(81), 37-47.

Baker, L., & Lombardi, B. R. (1985). Students' lecture notes and their relation to test performance. Teaching of Psychology, 12, 28-32.

Biggs, J. (1987) Student approaches to learning and studying. Hawthorn, Vic: Australian Council for Educational Research.

Brown, W. & Holtzman, W. (1967). Manual: Survey of study habits and attitudes. New York: Psychological Corp.

Britton, B. K., & Tesser, A. (1991). Effects of time management practices of college grades. Journal of Educational Psychology, 83, 405-410.

Crede, M., & Kuncel, N. R. (2008). Study habits, skills, and attitudes: The third pillar supporting collegiate academic performance. Perspectives on Psychological Science, 3, 425-454.

Dunn, R., & Dunn, K. (1978). Teaching students through their individual learning styles: A practical approach. Reston, VA: Reston Publishing.

Felder, R. M. & Silverman, L. K. (1988). Learning and teaching styles in engineering education. Engineering Education, 78(1), 674-681.

Gardner, H. (1993). Frames of mind: The theory of multiple intelligences. New York: Basic Books.

Gregore, A. F. (1984). Gregore style delineator: Development, technical, and administration manual. Gregore Associates, Inc.

Jung, C. G. (1970). Analytical psychology, theory and practice, the Tavistock lectures. New York: Vintage Books.

Kazu, . Y. (2009). The effect of learning styles on education and the teaching process. Journal of Social Sciences 5(2): 85-94.

Kern, C. W., Fagley, N. S., & Miller, P. M. (1998). Correlates of college retention and GPA: Learning and study strategies, testwiseness, attitudes, and ACT. Journal of College Counseling, 1, 26-35.

KoIb D. (1984). Experiential Learning: Experience as the Source of Learning and Development. Englewood Cliffs, New Jersey: Prentice Hall.

KoIb, D. A. (1985). Learning style inventory (3th edition). Boston: McBer.

KoIb, A. Y. & KoIb, D. A. (2005). Learning styles and learning spaces: enhancing experiential learning in higher education. Academy of Management Learning & Education, 4(2), 193-212.

Loo, R. (2004). Kolb's learning styles and learning preferences: Is there a linkage? Educational Psychology, 24(1), 99-108.

McCarthy, B. (1987). The 4MAT system: Teaching to learning styles with rightñeft mode techniques. Barrington, IL: EXCEL, Inc.

Proctor, B. E., Prevatt, F, Adams, K., Reaser, A., & Petscher, Y. (2006). Study skills profiles of normal-achieving and academically struggling college students. The Journal of College Student Development, 47, 37-51.

Sadler-Smith, E. (1996). Learning Styles: a holistic approach. Journal of European Industrial Training, 20, 29-36.

Salter, D. W., Evans, N. J. & Forney, D. S. (2006). A longtitudinal study of learning style preferences on the Myers-Briggs Type Indicator and Learning Style Inventory. Journal of College Student Development, 47(2), 173-184.

Schmeck, R. R., Geisler-Brenstein, E., & Cercy, S. P. (1991). Self-concept and learning: The revised inventory of learning processes. Educational Psychology 11: 343-362.

Weinstein, C. E., & Palmer, D. R. (2002). Learning and study strategies inventory. User's Manual (2nd ed.).

Wolfe, K., Bates, D., Manikowske, L. & Amundsen, R. (2006). Learning styles: Do they differ by discipline. Journal of Family and Consumer Sciences, 97(4), 18-22.

Author affiliation:



Center for Individual and Academic Development (CIAD)

Sabana University

Istanbul, Turkey

Author affiliation:

Corresponding Author

Aytac Ggogus E-mail(s): or, Phone: 90 216 483 9485 (office) & 90 539 723 5245 (cell), Fax: 90 126 483 9480

Short Biographies

Aytac Gogus has been an educational researcher and instructional designer at the Center for Individual and Academic Development, Sabanci University since September 2007. She received her doctoral degree in Instructional Design, Development, and Evaluation (IDD&E) at Syracuse University in May 2006.

Hatice Gunes has been a student counselor at the Center for Individual and Academic Development, Sabanci University since October 2007. She received her doctoral degree in psychology at Ankara University in October 2009.

The use of this website is subject to the following Terms of Use