Fostering Critical Thinking Through Debate in Veterinary Education: A Large Classroom Perspective
Bibliographic record
Abstract
As Doctor of Veterinary Medicine (DVM) class sizes continue to increase in the United States, there are concerns about maintaining student engagement and fostering higher-order cognitive skills within large classroom settings. Traditional didactic approaches in these environments may hinder student motivation and critical reasoning, skills essential for Day One-ready veterinarians. Although active learning strategies are recognized for enhancing student engagement and critical thinking, they are often perceived as difficult to implement in large classrooms due to logistical and instructional challenges. This article describes the implementation of a lecture hall-based debate session in a large pre-clinical veterinary classroom as an active learning strategy. Findings showed high student engagement, with nearly all participants actively involved. Students reported that the debate format facilitated recall of presession materials and improved learning, and encouraged them to seek additional resources, reinforcing integration of knowledge. Qualitative feedback highlighted the debate's role in developing individualized decision making and critical appraisal skills relevant to veterinary practice. This article supports the feasibility and effectiveness of debates as an active learning strategy in large classroom settings within the pre-clinical veterinary 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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".