Multicentric Survey on Learning Styles Between Members of the Veterinary Field
Why this work is in the frame
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Bibliographic record
Abstract
Teaching medical sciences is a continuously evolving process that requires an ongoing update for both students and teachers. Several methods are used to measure learning styles, among which the Visual, Auditory, Read/Write, Kinesthetic (VARK) framework focuses on how learners prefer to obtain information. With this study, we aimed to assess the VARK learning style on a large sample of veterinary students and educators in an aged-variety, multi-lingual, and multi-institutional setting. We obtained a total of 873 replies to our survey: 78.7% students, 6.6% veterinarians, 5.9% people with another occupation inherent to veterinary medicine, 5.7% European or American board-certified specialists, 1.1% veterinary nurses, 0.9% veterinary interns, and 0.9% veterinary residents of different specialties. The replies were obtained from French (56%), English (31.7%), Italian (11.5%), and Spanish (0.8%) versions of the survey. Most respondents (52.6%) were unimodal learners, while 47.4% exhibited two or more learning styles. Baby Boomers and Millennials were significantly less likely to use the visual and the aural style, respectively, compared with Generation Z. Moreover, Baby Boomers were approximately 54.2% less likely to be multimodal learners than Generation Z (χ 2 = 4.291, p = .038). According to our results, the current veterinary student population is comprised of multimodal learners highly adapted to learn visually and by listening, although there are some differences between countries. An initial assessment with the VARK survey at the beginning of the course may help teachers to study their specific population. Finally, here we collect some specific recommendations to follow based on the country where students are enrolled.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it