Retention following Two-Stage Collaborative Exams Depends on Timing and Student Performance
Bibliographic record
Abstract
Multistage collaborative exams are implemented to enhance learning and retention of course material. However, the effects of multistage collaborative exams on retention of course content are varied. These discrepancies may be due to a number of factors. To date, studies examining collaborative exams and content retention have used questions that all, or mostly, require students to select an answer, rather than generate one of their own. However, content retention can improve when students generate their own responses. Thus, we examined the effect of collaborative exams with open-ended questions on retention of course content. Retention was measured at two time periods; one relatively shortly (9 days) following a collaborative exam and another over a longer time period (23 days). Furthermore, we examined whether content retention differed for low-, mid-, or high--performing students. Our results suggest that collaborative exams offer retention benefits at relatively long time periods between pre- and posttests, but not over shorter time periods. Retention varied across students in different performance categories. Our study, the first to use only open-ended questions, showed relatively small effects compared with studies using multiple-choice or fill-in-the-blank format, but still suggest that collaborative exams can aid in 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 | 0.011 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".