Two-Stage (Collaborative) Testing in Science Teaching: Does It Improve Grades on Short-Answer Questions and Retention of Material?
Bibliographic record
Abstract
Two-stage collaborative testing is an assessment strategy that involves students initially writing a test individually and then immediately afterward writing the same (or similar) test again in groups. Current evidence shows that two-stage testing improves performance on multiple-choice tests as well as short-term retention of material, but little is known about the effect on long-answer questions and retention across a longer time frame. The purpose of this study was to determine (a) if two-stage testing improves performance on both multiple-choice and long-answer questions, (b) if two-stage testing improves short-and longterm knowledge retention, and (c) whether there are differences in knowledge retention based on question type. A two-stage midterm with both question types was administered in two undergraduate science courses, followed by a short-term and long-term retention test. Performance on both question types improved, with comparable improvement on both question types. Two-stage testing also maintained knowledge retention from the original midterm for both question types in the short term, although the learning gains for long-term retention were less apparent.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".