Teaching Veterinary Radiography by E-Learning versus Structured Tutorial: A Randomized, Single-Blinded Controlled Trial
Bibliographic record
Abstract
Case-based e-learning may allow effective teaching of veterinary radiology in the field of equine orthopedics. The objective of this study was to investigate the effectiveness of a new case-based e-learning tool, compared with a standard structured tutorial, in altering students' knowledge and skills about interpretation of radiographs of the digit in the horse. It was also designed to assess students' attitudes toward the two educational interventions. A randomized, single-blinded, controlled trial of 96 fourth-year undergraduate veterinary students, involving an educational intervention of either structured tutorial or case-based e-learning, was performed. A multiple-choice examination based on six learning outcomes was carried out in each group after the session, followed by an evaluation of students' attitudes toward their session on a seven-point scale. Text blanks were available to students to allow them to comment on the educational interventions and on their learning outcomes. Students also rated, on a Likert scale from 1 to 7, their performance for each specific learning outcome and their general ability to use a systematic approach in interpreting radiographs. Data were analyzed using the Mann-Whitney test, the t-test, and the equivalence test. There was no significant difference in student achievement on course tests. The results of the survey suggest positive student attitudes toward the e-learning tool and illustrate the difference between objective ratings and subjective assessments by students in testing a new educational intervention.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".