Effectiveness of a Student-Developed Instructional Video in Learning the Anatomy of the Equine Distal Limb
Bibliographic record
Abstract
The anatomy of the equine distal limb (EDL) is both complex and important to veterinary clinical practice. First-year veterinary students (VM1s) often struggle to adequately understand it. Two third-year veterinary students collaborated with instructors to create an instructional video to facilitate first-year students’ comprehension of EDL anatomy. The video was offered to all VM1s. Learning outcomes were assessed via practical exams. Exam scores on EDL structures were compared between students who did ( video) and students who did not ( no video) watch the video. Students’ laboratory experiences and confidence were evaluated with a post-exam survey. The third-year students documented their experiences while producing the video. Eighty percent of VM1s viewed the video; 91% rated the video as very valuable. The video improved student confidence during the practical exam by 9%, and 89% of surveyed students indicated the video positively impacted their exam grade. One item score was significantly improved in the video group ( p < .001), as was the score of the five questions combined ( p < .001). As expected, overall practical exam scores were not statistically different. Student collaborators indicated that participation reinforced their knowledge while enhancing their professional development. Student collaboration was a beneficial strategy for instructional support development that positively impacted student affect and also generated opportunities for the involved students’ professional growth.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".