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RESEARCH-ARTICLE
Department of Biology, San Francisco State University, San Francisco, California
Address for reprint requests and other correspondence: J. Breckler, Dept. of Biology, San Francisco State Univ., 1600 Holloway Ave., San Francisco, CA 94132 (e-mail: jbreck{at}sfsu.edu).
Abstract
Student learning may be classified according to the sensory modalities by which one prefers to take in information. One such classification scheme uses the VARK instrument, which categorizes learning preferences as visual (V), auditory (A), reading-writing (R), or kinesthetic (K). Many students have a single, strong preferences ("unimodal"), whereas others have multiple ("multimodal") learning preferences. Although limited in scope and reliability, knowledge of student learning preferences is important for reasons of pedagogy. Teaching and student learning styles may also affect student academic success in science coursework and fulfillment of student career goals. In our study, we determined the learning preferences of upper-division students in a human physiology course during a 2-yr period at a public undergraduate institution in California. We also sought to determine the association between individual learning styles and stated career intentions. We found that the majority of students interested in the health professions have multimodal learning preferences. Furthermore, a greater percentage of premedical students had multimodal preferences compared with predental and prescientist students. When data were compared by gender, we found that more female than male students had multimodal learning preferences. We also observed some gender differences when separating student groups by career choice. For example, more premedical men had multimodal preferences compared with nonpremedical men. In contrast to men, women showed little differences in their learning style profiles whether premedical or not and also self-predicted their learning preferences more accurately. Thus, career choice may be an important consideration in determining whether or not there are gender differences among students.
Key words: visual, auditory, read-write, kinesthetic; premedical students; adult learning
THE WAY(S) that we learn new information can be categorized according to our specific learning styles. There are now over a dozen major models that describe learning styles or learning "preferences" (2). While each model has its drawbacks, knowledge of learning preferences can help teachers know more about the students they teach and help them to develop effective curricular approaches (21). Learning style information can also benefit the students directly as they learn more about themselves and acquire knowledge of general learning theory (2, 23).
Among the ever-growing number of tools used to determine learning preferences, one instrument that has been used by students in the health professions is called the VARK instrument (10), which categorizes learning preferences that are based on the sensory modality by which one prefers to take in new information: visual (V), auditory (A), reading-writing (R), or kinesthetic (K). For example, students with a "V" learning preference enjoy seeing graphics, diagrams, pictures, or colored word accents. Students with "A" learning preferences prefer listening, interacting, and discussing. The "R" learning preference involves textual content such as reading textbooks, word lists, and text-based handouts. The "K" learning preference involves using one's body to physically touch or manipulate objects or materials. Some individuals have a single, strong ("unimodal") learning preference, whereas others have two to four "multimodal" preferences. These categorizations are helpful, although they represent only a partial description of the complex manner in which students perceive, process, and recall information.
The VARK instrument has a number of limitations in its scope, validity, and reliability. Even according to the VARK developer (10, 11), VARK is not a complete learning style inventory but rather provides users with a simple profile of their basic sensory learning preferences. In addition, VARK does not take into consideration other learning criteria that are clearly important in the science classroom setting, such as engagement, motivation, and enthusiasm.
Despite the limitations of VARK and all learning style instruments, there are new and exciting data that suggest that specific VARK learning profiles might be associated with those students pursuing careers in the health professions. The VARK tool was used to show the learning preferences of medical and dental students. Approximately 64% of the medical students in Michigan and Turkey had multimodal learning preferences (1, 19) compared with a slightly lower percentage (i.e., 56%) of dental students (22).
We expanded on these studies to address the question of whether undergraduate physiology students have learning preferences that resemble those of students who are already enrolled in health profession programs such as medical and dental schools. Furthermore, we wanted to determine whether or not premedical students have learning styles that are similar to those reported for medical students and whether predental student learning preferences resemble those of dental students.
We also addressed the question of whether a gender difference exists for learning preferences among premedical physiology students. A recently published report (27) has suggested that male and female physiology students in Michigan may have different learning preferences, although student career intentions were not considered. Our study calls into question some of the conclusions about gender reported in the Michigan study and also expands the general scope of inquiry in the following ways. In our study, we examined students enrolled over a much longer period (i.e., 4 semesters rather than 1 semester), and we report on a larger number of respondents (i.e., n = 218 compared with n = 48). We also report both raw and percentile data to enable the reader to have a closer look at the number of students being compared in each group. Most importantly, in our study, we determined the potential association of both gender and career choice with individual student learning preferences.
METHODS
Design. To determine the learning style preferences of undergraduates with specific science-related career intentions, we administered the VARK questionnaire developed by Neil Fleming to students enrolled in our Human Physiology course. We selected the VARK test (10) due to its ease of use, wide distribution in the field of education, and recent usage in studies of health profession students. VARK version 7.0 can also be taken online and consists of 13 questions (11).
The students who participated in the study were enrolled in a capstone one-semester Human Physiology lecture course at San Francisco State University (SFSU). SFSU is a large, urban, multicultural undergraduate public institution that is part of the 23-campus California State University system, the largest university system in the country. At the SFSU campus, the current enrollment is 
24,000 undergraduate students and 6,000 graduate students in various master's programs.
The Human Physiology course (Biology 612) is an upper-division course and enrolls mainly biology or other science majors. Students have already completed prerequisite courses of introductory biology (1 yr), physics (1 semester), and chemistry (3 semesters) including organic chemistry. Data were collected near the beginning of each semester over a 2-yr period (spring 2006, fall 2006, spring 2007, and fall 2007). Students were mainly of junior and senior undergraduate status plus some postbaccalaureate and graduate students.
Procedures. We administered the VARK questionnaire (10) together with our accompanying survey form in hard copies. Our basic survey form included a series of demographic questions such as gender and age. Students were prompted to select their "intended career" from a list of 21 career choices, including "other (nonscience related)," "other (science related)," or "other" with a write-in blank. Students were asked whether or not they had previously answered questions to determine their learning style.
Students were also asked to self-assess their learning preferences by selecting the following one or more statements (which were later coded by us): 1) "seeing text or diagrams helps me to take in new information" (coded as V); 2) "hearing the material helps me to take in new information" (coded as A); 3) "writing down what I hear or read helps me to take in new information" (coded as R); and 4) "touching or observing a physical model helps me to take in new information" (coded as K).
All student information was collected anonymously, and each student created a fictitious personal identifier on the forms. Approximately 2–4 wk later, we provided the instructor and students with the learning style results along with general information about basic learning styles with suggested study strategies from the VARK website. This study was reviewed and approved by the Committee for the Protection of Human Subjects of the Institutional Review Board at SFSU.
Analysis. In our reporting of the VARK data, we report students as having one strong (unimodal) learning preference (i.e., either V, A, R, or K) or multimodal (i.e., 2–4) learning preferences. Data (reported as percentages) were obtained by dividing the number of students by the total number of respondents.
RESULTS
Physiology student learning preferences. During the entire 2-yr period of the study, the one-semester Human Physiology course enrolled a total of 441 students over four sequential semesters (i.e., spring 2006, fall 2006, spring 2007, and fall 2007). A total of 218 student respondents filled out the survey and VARK instrument, giving an overall 49.4% class response rate. There were practically no students repeating this class; thus, each semester represents a unique group of respondents.
We found that the majority of respondents in our study had multimodal VARK learning preferences. Semester-by-semester results for the percentage of respondents were as follows: fall 2007 (61.4%), spring 2007 (69.4%), fall 2006 (61.7%), and spring 2006 (53.8%). The remaining students obviously preferred a single mode of learning (i.e., either V, A, R, or K) as follows: fall 2007 (38.6%), spring 2007 (30.6%), fall 2006 (38.3%), and spring 2006 (46.2%). The class in spring 2007 had the lowest percentage of students with a unimodal learning preference, yet with a very low response rate, rendering that particular semester less representative of the physiology course. The other three classes showed little variability during the 2-yr period (i.e., range of 38.3–46.2%). Thus, we decided to pool all data from all four semesters (n = 218 students) for the remaining data analysis.
The data shown in Fig. 1 show that the majority of respondents had two to four (multimodal) learning preferences (60%) and the remaining 40% of the students had one strong learning preference. The percentages of students preferring a single mode in descending order were K (16%), R (15%), A (5%), and V (4%). Thus, the K learning preference was the most common unimodal learning preference. The percentage of students with a K learning preference ranged from 5.6% to 26.2% during the four semesters of our study.
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Career choices and learning preferences. To determine the association of career choices with student learning preferences, we tabulated the survey career results as shown in Table 1. The greatest number of physiology students aspired to a career in medicine and selected "physician." We then compared each individual's VARK learning style preferences with their stated career choice. The most commonly stated career choices and the percentage of those students with unimodal versus multimodal preferences are shown in Fig. 2. Among the students in the most commonly stated careers, premedical students (i.e., students who stated "physician") had the highest percentage of those with two to four multimodal learning preferences.
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When we asked students for their self-perception of their personal learning style preference on our survey form, we found that the vast majority of students predicted they would have multimodal learning preferences. Approximately 80.7% of the students self-predicted that they would have more than one preference, whereas only 19.3% of them self-predicted a single unimodal learning preference. Yet, the actual results obtained from the VARK instrument revealed that twice as many students had a unimodal learning preference than was self-predicted by the students (i.e., 39.5% had a single unimodal preference compared with 19.3% self-prediction). Interestingly, students with multimodal preferences did not necessarily self-predict any more accurately. For instance, 32 of 218 (14.7%) of the students predicted themselves as having all four learning preferences (i.e., VARK), yet only roughly a third (37.5%) of those students correctly self-predicted.
Overall, only 15.5% of all students were able to accurately self-predict their own VARK result, although the majority of students (i.e., 61%) had answered questions about their learning styles in the past. A further breakdown of the prediction data by gender showed that females were nearly three times better at self-prediction than males (i.e., 18.8% vs. 6.8% correctly self-predicted). Students with multimodal preferences were generally much better predictors than those with a unimodal preference. The best predictors were the female quadmodal VARK group. When the data were analyzed according to career choices, premedical female students self-predicted their learning preferences nearly three times more accurately than premedical male students (22.6% vs. 8%). Similarly, pooled nonpremedical students showed the same gender trend, with females outpredicting males (16.8% vs. 5.9%).
DISCUSSION
More than half of all prehealth professions students in our study (i.e., premedical, predental, preveterinarian, and prepharmacy) had multiple learning preferences with the VARK instrument. These data are roughly within the range of values reported for the tens of thousands of users of the VARK website (11), of whom 58% have multimodal learning preferences. In contrast, only 45% of our preresearch career-bound students have multimodal preferences, which suggests there are differences between learning preference profiles among prehealth and prescientist students.
We also observed some variation among students having different health career aspirations. Our results show that premedical students are similar to first-year medical students in their learning preferences in that both groups have large numbers of multimodal learners [i.e., 69% of premedical students and 64% of medical students (1, 19)]. We also found that students interested in dentistry also resemble their professional school counterparts in that slightly fewer students have multimodal learning preferences [i.e., 42% of predental students and 56% of dental students (22)]. Although published data on pharmacy student learning styles are not available, we show here that prepharmacy students have a very high percentage of students with all four learning preferences. Our expectation was that prepharmacy students would be more technically or quantitatively oriented than other prehealth students and perhaps would have strong visual preferences for graphs and charts. We were surprised to find that many prepharmacy students have strong multimodal preferences, especially compared with premedical students.
A previous study on undergraduate physiology students with unknown career intentions in Michigan (27) showed that learning style preferences may be related to gender. In this study, a higher percentage of males had multimodal learning preferences, i.e., 54.2% of males compared with 20.8% of females. Yet, we found opposite results in our large study of California physiology students using the same learning style instrument. Indeed, if a gender difference even exists, we found that there are slightly more females with multimodal preferences compared with males. However, we did observe some interesting gender differences when separating student groups by career choices. For the premedical students, slightly more males than females had multimodal preferences (i.e., 76% of males compared with 64% of females). Also, when premedical students were compared with nonpremedical students, more premedical men had multimodal preferences. In contrast, women showed little difference in their learning style profiles, whether premedical or not. Thus, career choice may be an important consideration in determining whether or not there is any gender difference in student learning style preferences. Obviously, our overall data do not resolve the debate regarding a gender gap in science (12). Yet, our results agree most closely with reports that showed a slight or insignificant gender difference among both first-year medical students (1, 25) and community college students in various disciplines, including science (15).
Limitations of the VARK instrument. The validity and reliability of the VARK questionnaire is clearly a limitation in studies on learning preferences (27). The value of the questionnaire in predicting science student learning is but one of a myriad of factors. Yet, individual learning preferences that appeal to one's auditory, visual, and kinesthetic senses can be an important consideration for educators. The VARK learning philosophy at least offers and encourages teachers to acknowledge that are learning differences and to make efforts to address some of these differences in their classrooms by attempting a wide range of teaching approaches (7).
A strength of the VARK data is that knowledge of prehealth student learning styles can be used to inform pedagogy, particularly if students are not being taught in a manner consistent with their current preferences. For instance, the vast majority of physiology students in our study preferred the K learning style among their preferences, in even greater numbers than those that preferred V, R, and A learning styles. This suggests that teaching approaches that incorporate the K learning style might be very useful and improve learning and engagement among many students. For those students with a strong unimodal K learning preference, learning difficult subject matter could be especially challenging and render some students at a learning disadvantage in traditional didactic lectures. To provide K learning style opportunities, the instructor might include active learning strategies (8) or engage students in tactile demonstrations or in directly manipulating objects (5, 16, 24). Instructors could also ask students to participate and experience actual physiological processes during lecture, especially in physiology courses that lack a laboratory component. For example, students can monitor their own pulses and respiratory rates, contract their own muscles, perform simple neurological tests using reflexes or sensory stimuli, and perform breath holding, all from the comfort of their own seat. Creative methods might include having students act out the cardiac cycle using the classroom as the chamber of a "ventricle" and the doors as "valves." Formative (i.e., ungraded) kinesthetic homework assignments could be encouraged, such as monitoring body temperature, performing diuresis using water/salt/caffeine, measuring pulse before/after rising from a supine position, altering sugar intake, and measuring pulse or ventilatory rates with exercise.
Future directions. Our data suggest there may be certain learning style profiles for premedical and other prehealth professions students. However, one cannot assume that learning styles are fixed (2, 13). We certainly do not suggest that learning preferences are predictive of successful matriculation into medical or dental schools. In fact, due to our anonymous survey, we do not know which individuals were successful in gaining admittance to professional schools, their grades achieved, and/or whether they made significant progress toward or even changed their career objectives. It would be interesting to track individual students admitted to medical, dental, or other graduate or professional schools to see if there is a trend in learning preferences for individuals and whether or not these preferences remained the same once students were enrolled in health profession programs.
Successful matriculation to a health profession schools is certainly dependent on academic achievement and learning in undergraduate science coursework. To ensure that all students have equal access to health profession careers, knowledge of their learning preferences might have some impact on student academic achievement, either directly or indirectly. Providing information about learning strategies may be useful to the students, instructors, and prehealth advisors and may even help meet the nation's imperative to ensure diversity in the healthcare workforce (14, 18, 20, 26).
Conclusions. Learning theory can provide useful information to physiology instructors. In the classroom, a wide variety of teaching methods can help reach the diversity of learners. Many novel teaching approaches have expanded the number of tools that are of potential benefit and improve student enjoyment (6, 17). However, there is widespread disagreement as to whether the style of teaching should be tailored to student learning styles and, indeed, which learning style model should inform one's teaching (4). The main usefulness of learning style information may be primarily to the students themselves and, at the very least, provide vocabulary to help them define appropriate learning strategies (2, 3, 9).
Simply recognizing that there are different styles of learning and evolving one's repertoire of learning strategies may be particularly relevant for prehealth students desiring careers as clinicians and health professionals (22). Health professions usually require several simultaneous skills involving sensory components such as visual (i.e., deciphering graphic content in research articles), auditory (i.e., listening to patients or clients), reading-writing (i.e., reading journal articles and keeping records), and kinesthetic (i.e., learning or performing physical exams and procedures). Thus, in addition to improving their academic performance, knowledge of learning theory may help prehealth students become aware of and develop ways to master these lifelong professional skills.
Acknowledgments
The authors thank Kemi Role, Mimi Park, Amanda del Rosario, and Pamela Pablico for helping to gather and organize course data. We acknowledge instructors T. Mau and S. Sassi for allowing their classrooms to participate in this study and Neil Fleming for permission to use the VARK questionnaire.
Received for publication March 10, 2008. Accepted for publication December 20, 2008.
REFERENCES
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