Effect of using an audience response system on learning environment, motivation and long-term retention, during case-discussions in a large group of undergraduate veterinary clinical pharmacology students
Bibliographic record
Abstract
BACKGROUND: Teaching methods that provide an opportunity for individual engagement and focussed feedback are required to create an active learning environment for case-based teaching in large groups. AIMS: A prospective observational controlled study was conducted to evaluate whether the use of an audience response system (ARS) would promote an active learning environment during case-based discussions in large groups, have an impact on student motivation and improve long-term retention. METHODS: Group A (N = 83) participated in large group case discussions where student participation was voluntary, while for group B (N = 86) an ARS was used. Data collection methods included student and teacher surveys, student focus group interviews, independent observations and 1-year post-course testing. RESULTS: Results indicated that the use of an ARS provided an active learning environment during case-based discussions in large groups by favouring engagement, observation and critical reflection and by increasing student and teacher motivation. Although final exam results were significantly improved in group B, long-term retention was not significantly different between groups. CONCLUSIONS: It was concluded that ARS use significantly improved the learning experience associated with case-based discussions in a large group of undergraduate students.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".