Use of a Multimodal, Peer-to-Peer Learning Management System for Introduction of Critical Clinical Thinking to First-Year Veterinary Students
Bibliographic record
Abstract
Veterinary medical students need multiple thinking strategies, particularly critical thinking. We used a multimedia, peer review learning management system (CGScholar) to introduce a series of complex, realistic, case-based e-learning modules to help introduce critical thinking to 422 first-year veterinary students through instructor-designed clinical cases. Students developed and published on the CGScholar platform an analysis of a case and conducted anonymous peer reviews of each other's drafts. Instructors selected desirable characteristics of a student's activity to track and provide automatic feedback to students via an analytics dashboard and aster plot that allowed visualization of progress. The dashboard also enabled instructors to view the entire class's performance, highlighting students whose performance was lagging. Online interactions were supplemented by case-specific face-to-face workshop sessions. Our goal was to address the following questions: Does the addition of multimedia to a work (one's own or others') enhance people's ability to understand and convey the material? Does peer review (of one's own and others' work) lead to improvements in the writer's own work? Does the peer review process enhance the writer's understanding of what constitutes high-quality literature evidence? An anonymous student survey showed that experience was significantly more positive in the second and third year of implementation after inclusion of explicit guidance on the use of the rubric for peer review. Overall, 67% of students thought inclusion of multimedia enhanced their ability to communicate and 52% agreed multimedia enhanced their ability to understand their peers' analyses, but students were split on benefits to their understanding of high-quality literature.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".