Incorporating an Audience Response System into Veterinary Dermatology Lectures: Effect on Student Knowledge Retention and Satisfaction
Bibliographic record
Abstract
Veterinary educators are charged with delivering large amounts of information to adult students, who benefit from a more interactive learning environment than is often achieved through didactic lectures. Audience response systems (ARS) with wireless keypad technology facilitate interactive learning and have been used successfully in the education of health professionals. The objectives of this pilot study were to determine the effect of an ARS on the knowledge retention of veterinary dermatology students and to survey student attitudes concerning its use. A cohort-controlled trial was conducted to evaluate the potential benefits of ARS for short-term and long-term knowledge retention. Students also participated in four hours of student-directed case simulations using ARS technology. Students were surveyed regarding opinions on the use of the ARS. The mean short-term knowledge-retention test scores of groups A (ARS+) and B (ARS-) were 81% and 78%, respectively. The mean long-term knowledge-retention test scores of groups A and B were 54% and 55%, respectively. The differences between groups were not significant for either time period (p = 0.32, p = 0.77). Although benefits to short-term and long-term knowledge retention were not detected in this pilot study, all students responding to the survey perceived a benefit and supported the use of ARS in the clinical veterinary curriculum. ARS technology provides a tool for lecturers to create an interactive learning environment well suited for teaching veterinary dermatology.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".