Answering questions in a co‐created formative exam question bank improves summative exam performance, while students perceive benefits from answering, authoring, and peer discussion: A mixed methods analysis of PeerWise
Bibliographic record
Abstract
Multiple choice questions (MCQs) are a common form of assessment in medical schools and students seek opportunities to engage with formative assessment that reflects their summative exams. Formative assessment with feedback and active learning strategies improve student learning outcomes, but a challenge for educators, particularly those with large class sizes, is how to provide students with such opportunities without overburdening faculty. To address this, we enrolled medical students in the online learning platform PeerWise, which enables students to author and answer MCQs, rate the quality of other students' contributions as well as discuss content. A quasi-experimental mixed methods research design was used to explore PeerWise use and its impact on the learning experience and exam results of fourth year medical students who were studying courses in clinical sciences and pharmacology. Most students chose to engage with PeerWise following its introduction as a noncompulsory learning opportunity. While students perceived benefits in authoring and peer discussion, students engaged most highly with answering questions, noting that this helped them identify gaps in knowledge, test their learning and improve exam technique. Detailed analysis of the 2015 cohort (n = 444) with hierarchical regression models revealed a significant positive predictive relationship between answering PeerWise questions and exam results, even after controlling for previous academic performance, which was further confirmed with a follow-up multi-year analysis (2015-2018, n = 1693). These 4 years of quantitative data corroborated students' belief in the benefit of answering peer-authored questions for learning.
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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 | 0.022 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".