Making Clinical Case-Based Learning in Veterinary Medicine Visible: Analysis of Collaborative Concept-Mapping Processes and Reflections
Bibliographic record
Abstract
The value of collaborative concept mapping in assisting students to develop an understanding of complex concepts across a broad range of basic and applied science subjects is well documented. Less is known about students' learning processes that occur during the construction of a concept map, especially in the context of clinical cases in veterinary medicine. This study investigated the unfolding collaborative learning processes that took place in real-time concept mapping of a clinical case by veterinary medical students and explored students' and their teacher's reflections on the value of this activity. This study had two parts. The first part investigated the cognitive and metacognitive learning processes of two groups of students who displayed divergent learning outcomes in a concept mapping task. Meaningful group differences were found in their level of learning engagement in terms of the extent to which they spent time understanding and co-constructing knowledge along with completing the task at hand. The second part explored students' and their teacher's views on the value of concept mapping as a learning and teaching tool. The students' and their teacher's perceptions revealed congruent and contrasting notions about the usefulness of concept mapping. The relevance of concept mapping to clinical case-based learning in veterinary medicine is discussed, along with directions for future research.
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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 | 0.011 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".