Exploring Veterinary Medicine Students’ Experiences with Team-Based Learning at the Universidad Andrés Bello
Bibliographic record
Abstract
This study explored the use of team-based learning (TBL) in a Clinical Anatomy course taken by third-year veterinary medicine undergraduate students at the Universidad Andrés Bello in Chile. While research has shown that active learning methodologies yield improved student learning outcomes compared to lecture-based teaching, the incorporation of new pedagogical strategies is complex and its success depends on a range of contextual factors. This study sought to understand the strengths and weaknesses of using TBL in a specific subject (anatomy), discipline (veterinary medicine), and country (Chile). Students in the course had not been previously exposed to TBL. At the end of the semester during which TBL was used, the research team collected student satisfaction survey data and conducted a focus group in order to understand students' experiences with TBL in the course. We found that overall, students were satisfied with TBL and appreciated that it increased the amount of feedback they received, reinforced key concepts, and helped them to build skills they would need in their future professions. There was also a certain level of dissatisfaction, which may have been caused by negative experiences with team members and difficulties reading the preparatory material, which was in English. Given our findings, we discuss modifications that could be made in order to improve veterinary medicine students' experiences with TBL.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".