Investigation into the Impact of Audience Response Devices on Short- and Long-term Content Retention
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
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.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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