Introducing Peer-Assisted Learning into a Veterinary Curriculum: A Trial with a Simulator
Bibliographic record
Abstract
Peer-assisted learning (PAL) was implemented in the context of delivering training with a simulator, the Haptic Cow. This project was undertaken as a way of increasing student access to the simulator and to investigate the possibility of using PAL more extensively in the curriculum. Peer tutors attended a workshop to learn basic teaching skills and were then trained to use the simulator. The tutors taught their peers the basic skills for bovine rectal palpation with the simulator. The PAL project was evaluated using questionnaires and a focus group to gather feedback from both tutors and learners. Sixteen peer tutors trained 99 fellow students with the simulator. Both tutors and learners thought that there were certain advantages in students, rather than veterinarians, delivering the training. Student tutors were less intimidating and could relate more closely to the difficulties of their peers. However, lack of knowledge was identified as a potential issue. Students reported certain benefits from their role as tutors, including improvements in communication skills, knowledge of the subject area, and confidence in performing bovine rectal palpation. Additionally, the skills developed, including learning to teach, were considered to be useful for their future careers as veterinarians. Tutors and learners supported the continued use of PAL both with the simulator and in other areas of the course. The trial of PAL proved a successful way of delivering simulator-based training and the project has provided a basis for the further use of PAL in our curriculum.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".