Multimodal Integration of Active Learning in the Veterinary Classroom
Bibliographic record
Abstract
Historically, pre-clinical professional veterinary instruction has relied heavily on didactic methods. With the shift toward competency-based education in health professions teaching, instructors at The Ohio State University College of Veterinary Medicine are exploring alternative engagement strategies to focus on learner outcomes. In this article, we report on the integration of competency-based active learning techniques in a large-lecture setting, along with preliminary outcomes from the student perspective. A total of 110 students from Zoonotic Diseases, a two-credit core course offered in the second year of the 4-year professional curriculum, participated in the learning techniques and retrospective pre-/post-questionnaire. Results of the questionnaire indicated improvement in learners' perceived competency. For practical skills (e.g., donning and doffing of personal protective equipment), students also reported improved self-efficacy. Students enjoyed the interactive and self-directed learning techniques and described an improvement in their ability to evaluate their own understanding of relevant course concepts. The active learning techniques described herein may be used to supplement, and even transform, primarily lecture-based courses to better achieve professional competency and develop practice-ready veterinarians.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".