Design and Validation of a Computer-Aided Learning Program to Enhance Students' Ability to Recognize Lameness in the Horse
Bibliographic record
Abstract
The ability to recognize lameness in the horse is an important skill for veterinary graduates; however, opportunities to develop this skill at the undergraduate level are limited. Computer-aided learning programs (CALs) have been successful in supplementing practical skills teaching. The aim of this study was to design and validate a CAL for the teaching of equine lameness recognition (CAL1). A control CAL was designed to simulate learning by experience (CAL2). Student volunteers were randomly assigned to either CAL and tested to establish their current ability to recognize lameness. Retesting occurred both immediately following exposure and 1 week later. At each test point, the number of correct responses for forelimb and hind limb cases was determined. Student confidence was assessed before and after CAL exposure, with previous opportunities to recognize lameness taken into account. Immediately following exposure, the number of correct responses was significantly higher for CAL1 than for CAL2, both overall and for forelimb cases but not for hind limb cases. After 1 week, the CAL1 group performed significantly better overall compared to the CAL2 group, with no significant difference between forelimb and hind limb cases. Student confidence and ability to recognize lameness were significantly improved following exposure to CAL1. When considered as one category, students in years 4 and 5 performed significantly better than year 3 students. Gender did not significantly affect performance. CAL1 could be used to supplement current lameness recognition opportunities. CAL1 is, however, limited in its ability to improve lameness recognition, especially in relation to hind limb lameness where it was unable to attain a significant difference from CAL2.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".