Case-Based Learning: An Analysis of Student Groupwork and Instructional Design that Promotes Collaborative Discussion
Bibliographic record
Abstract
The use of small-group collaborative case-based learning methodologies has been growing both in interest and implementation across veterinary college curriculums in recent years. The ability of this pedagogical approach to solidify and deepen learning outcomes is well-established in the broader education literature. However, to achieve this positive impact, students must interact in productive discussions that expand the scope of their understanding. The present study focused on analyzing the ways in which professional veterinary students interact as they work collaboratively through clinical cases, in the context of a Team-Based Learning-intensive curriculum. This data was used to draw connections between the questions posed in the clinical case activities and the resulting intragroup collaborative outcomes, which can assist veterinary educators in sparking more robust student discussions through facilitation and instructional design. Fourteen participants formed two student groups that worked on 49 case questions across five sessions, providing 98 episodes of collaboration for analysis. The findings of this study revealed how professional veterinary students negotiated perspectives to come to consensus on in-class case-based learning tasks, including eight primary types of statements they made and seven overall patterns of group collaboration. This study highlighted specific elements of instructional design that influenced student collaboration including: allowing for multiple perspectives, sparking disagreement, perceived difficulty, learning outcome level, and the level of consensus required by the question structure. We present specific recommendations for veterinary educators to consider while designing questions for veterinary student groups.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".