Investigation into the Impact of Audience Response Devices on Short- and Long-term Content Retention
Bibliographic record
Abstract
Audience Response Systems (ARSs) may enhance short-term knowledge retention. Long-term knowledge retention is more difficult to demonstrate. According to previous studies, ARS questions requiring application of knowledge or peer interaction are more effective in maintaining student attention. The purpose of this study was to determine if peer discussion or individual-knowledge questions enhance short- and/or long-term knowledge retention. Third-year veterinary students responded to ARS questions posed in individual knowledge (n=3 questions) and peer discussion (n=3 questions) format from six different instructors. To test short-term memory, the same questions were delivered during the course examination (within 21 days). To test long-term retention, these questions were posed during a retention exercise (four months later). On the course examination, students had a higher (p<.01) probability (±SE) of correctly answering ARS individual-knowledge questions (93.8 ± 1.8%) compared to novel (previously unseen, non-ARS control) course examination questions (87.5 ± 3.1%), but the probability of correctly answering examination questions previously posed using ARS peer discussion format (89.5 ± 3.0%) did not differ from individual knowledge or novel examination questions. The positive impact of ARS-knowledge questions was not maintained through the retention exercise. Neither individual knowledge (70.5 ± 6.4%) nor peer-discussion questions (67.5 ± 6.9%) performed better on the retention exercise than the questions that appeared only on the course examination (68.6 ± 6.1%). Curricular strategies that emphasize content review may be more powerful than strategies that strengthen initial learning for long-term content retention.
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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 | 0.023 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".