Multicentric Survey on Learning Styles Between Members of the Veterinary Field
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 (χ2 = 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".