Multimodal Integration of Active Learning in the Veterinary Classroom
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.002 | 0.004 |
| 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) | 0.000 | 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