Students as Teachers in an Anatomy Dissection Course
Bibliographic record
Abstract
One way to improve students' learning outcomes and well-being is to change teaching practices to allow students to become more active participants. We used an anatomy dissection course to test a cooperative group work method in which first-year veterinary students took turns leading their peer group and were each responsible for teaching the anatomy of a particular topographic region. The important blood vessels, lymphatic system, and nerves of each region of the body were covered. Students felt that exploration of the entire topographic region helped them to acquire a comprehensive understanding of the respiratory apparatus and the cardiovascular and nervous systems. Assigning individual tasks to each group member resulted in sharing the workload equally. Open-ended comments revealed that support from other group members was important for the students' learning experience, but the results also offered insight into a lack of constructive criticism. While teaching was considered challenging, and even a stress factor that hindered learning for some students, group work was generally held to be supportive of learning. The results suggest that more thorough instruction of students in their group work and in their individual tasks is required. Some students experienced difficulty in expressing their concerns openly and in seeking guidance from teachers, demonstrating the need for further investigation regarding students' self-regulation skills. Comments from the open-ended responses suggest that use of a cooperative learning method in anatomy dissection courses not only deepens student understanding of a subject but also offers first-year students an opportunity to practice the generic skills that will be needed in their future profession.
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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 | 0.005 | 0.014 |
| 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 | 0.002 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".