Incorporating an Audience Response System into Veterinary Dermatology Lectures: Effect on Student Knowledge Retention and Satisfaction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it