Collaborative Testing Improves Performance on Long Answer Questions, and Maintains Long‐Term Retention of Course Material
Bibliographic record
Abstract
Collaborative testing (CT) is an assessment strategy whereby students first write a test as an individual and then immediately following, write the same test again as a group, with the opportunity to discuss their thought process in reaching their answers. This strategy has been shown to increase performance on multiple‐choice (MC) tests, and to improve short‐term retention of material. However, this format has not been evaluated for tests using long answer (LA) questions, and measures of improved long‐term retention are inconsistent. The purpose of this study was to determine if CT improves performance on LA questions and whether CT could improve long‐term retention of course material compared to traditional teaching and testing methods. Two courses, (3 rd year Exercise Physiology, n=102 and 2 nd year Biochemistry, n=64) administered identical protocols which included an in‐class collaborative midterm (half MC and half LA questions), an unannounced, individual retention test 1 week later (short‐term) followed by a brief instructor‐led, in‐class review of the test, and finally another unannounced individual retention test 6 weeks later (long‐term). Performance was calculated as the difference in grades between collaborative and individual midterm. Retention was calculated as the difference in grades between a given retention test and the individual midterm. CT improved performance on both MC and LA questions, but the degree of improvement was greater on LA questions (16.5% ± 1.29%, 20.6% ± 1.41%, p<0.05). As expected, short‐term retention was better on questions that had been tested collaboratively compared to questions that were only seen individually (+3.7% ± 1.53% vs. −7.9% ± 1.50%, p<0.05). Surprisingly, long‐term retention was similarly maintained for both collaborative and individual questions (+0.69% ± 1.92% vs. −0.16% ± 1.89%), indicating that retention of the questions that had been tested individually had improved since the short‐term retention test. Our results show that CT can improve performance on LA questions and help students retain this information over several weeks, but also suggests that taking the time to review a test in class may be an equal strategy to improve long‐term retention of material.
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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 | 0.002 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".