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| EDUCATION FORUM |
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| Year : 2014 | Volume
: 60
| Issue : 2 | Page : 183-186 |
Understanding your student: Using the VARK model
IJ Prithishkumar, SA Michael
Department of Anatomy, Christian Medical College, Vellore, Tamil Nadu, India
Correspondence Address:
Dr. I J Prithishkumar
Department of Anatomy, Christian Medical College, Vellore, Tamil Nadu
India
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0022-3859.132337
Background: Students have different preferences in the assimilation and processing of information. The VARK learning style model introduced by Fleming includes a questionnaire that identifies a person's sensory modality preference in learning. This model classifies students into four different learning modes; visual (V), aural (A), read/write (R), and kinesthetic (K). Materials and Methods: The 16-point multiple choice VARK questionnaire version 7.1 was distributed to first year undergraduate medical students after obtaining permission for use.Results: Seventy-nine students (86.8%) were multimodal in their learning preference, and 12 students (13.8%) were unimodal. The highest unimodal preference was K-7.7%. Surprisingly, there were no visual unimodal learners. The commonest learning preference was the bimodal category, of which the highest percentage was seen in the AK (33%) and AR (16.5%) category. The most common trimodal preference was ARK (8.9%). The total individual scores in each category were V-371, A-588, R/W-432, and K-581; auditory and kinesthetic being the highest preference. Visual mode had the lowest overall score. There was no significant difference in preference between the sexes. Conclusion: Students possess a wide diversity in learning preferences. This necessitates teachers to effectively deliver according to the needs of the student. Multiple modalities of information presentation are necessary to keep the attention and motivation of our students requiring a shift from the traditional large-group teacher-centric lecture method to an interactive, student-centric multimodal approach.
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