Assessment of Learning Style Preferences Among Medical Students and Their Correlation with Academic Performance in First-Year MBBS Subjects
Bibliographic record
Abstract
Background:Learning styles represent different approaches to learning that can influence how effectively students assimilate information. The VARK model classifies learning styles into four modalities: Visual, Aural, Read/Write, and Kinesthetic. Recognising students' preferred learning styles may help to optimise educational strategies, particularly in demanding fields such as medical education. Aim:This study aimed to identify the predominant learning style preferences among first-year MBBS students and to evaluate any associations between these preferences and their academic performance. Methods:A cross-sectional survey was conducted among second- and third-year medical students who had completed their first-year MBBS examinations. Data on age, sex, and subject-specific marks were collected, and the VARK (Version 8.01) questionnaire was administered to determine individual learning style preferences. Statistical analyses compared the distribution of preferred learning style by sex and by academic achievement, with significance assessed using appropriate software. Results:Among 126 participants (70 females, 56 males), quadmodal (multimodal) was the most commonly reported preference (33.3%), followed by unimodal (33.3%), bimodal (16.7%), and trimodal (16.7%) learning styles. The Kinaesthetic modality was the predominant unimodal preference. No significant differences in learning style distribution were observed between male and female students. Academic performance did not significantly correlate with any specific learning style; high and low performers exhibited similar distributions of learning preferences. Conclusion:Multimodal learning styles are the most prevalent among first-year MBBS students, and there is no significant association between learning style preference and academic achievement. These results underscore the value of incorporating diverse teaching methods to accommodate various learning preferences within the medical curriculum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".