Case-Based e-Learning Experiences of Second-Year Veterinary Students in a Clinical Medicine Course at the Ontario Veterinary College
Bibliographic record
Abstract
Exposure to real-life clinical cases has been regarded as the optimal method of achieving deep learning in medical education. Case-based e-learning (CBEL) has been considered a promising alterative to address challenges in the availability of teaching cases and standardizing case exposure. While the use of CBEL has been positive in veterinary medical education, insight into students' learning experience with a CBEL tool have not been considered. This article investigates students' views around the utility and usability of a CBEL tool, as well as perceived effectiveness, clinical confidence, and impact of veterinary students' learning preferences on CBEL use. Through focus groups as well as pre- and post-use questionnaires, students expressed that the design and utility of the online cases, including their authenticity, played an instrumental role in perspectives and acceptance of the CBEL tool. Students perceived the CBEL tool as highly effective in both achieving CBEL outcomes and teaching a methodical approach to a clinical case. CBEL elements were also perceived to potentially contribute to increased clinical confidence after CBEL use. Additionally, exploration of students' preferred approach to learning revealed that hands-on learners and those who prefer to learn by practicing and applying knowledge were more likely to show positive perceptions of a CBEL tool. This article's findings can help guide educators in the future design and implementation of online cases in various capacities and provide a platform for further exploration of the effectiveness and use of CBEL in veterinary medical education.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".