The Use of Adaptive Learning Technology to Enhance Learning in Clinical Veterinary Dermatology
Bibliographic record
Abstract
Clinical teaching in veterinary medicine is challenging for both educators and students. There is an increasing interest in the use of technology-based techniques using adaptive learning to provide students with additional learning experiences. Few studies have evaluated the use of this technique in veterinary medical education. We hypothesized that students with access to adaptive learning modules during dermatology rotation would have significantly higher dermatology test scores compared to students who did not have access to the adaptive learning modules on the same rotation. Incoming third and fourth-year veterinary students to the dermatology rotation, who agreed to participate, were randomly assigned to treatment (provided access to 10 modules using adaptive technology during the rotation) or control group (provided no access to the modules). Study participants completed a pretest two weeks before the rotation start date and a post-test near the rotation end date and a questionnaire to assess students’ learning experience using adaptive learning modules. Students in the treatment group scored significantly higher on the posttest ( p = .019) compared to students in the control group, with an effect size of d = 0.83. Students in both groups scored significantly higher at post-test ( p < .001; d = 1.52 treatment and p = .002; d = 0.74 control) when compared to their pretest. This study shows that the tested adaptive learning platform may be an effective method to augment clinical teaching in veterinary dermatology. This study also indicates that veterinary students perceive the use of adaptive learning technology as beneficial for their education.
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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 | 0.001 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".