Medical school 2.0: How we developed a student-generated question bank using small group learning
Bibliographic record
Abstract
BACKGROUND: The multiple-choice question (MCQ) is one of the most common methods for formative and summative assessment in medical school. Common challenges with this format include (1) creating vetted questions and (2) involving students in higher-order learning activities. Involving medical students in the creation of MCQs may ameliorate both of these challenges. What we did: We used a small group learning structure to develop a student-generated question bank. Students created their own MCQ based on self-study materials, and then reviewed each other's questions within small groups. Selected questions were reviewed with the class as a whole. All questions were later vetted by the instructor and incorporated into a question bank that students could access for formative learning. Post-session survey indicated that 91% of the students felt that the class-created MCQ question bank was a valuable resource, and 86% of students would be interested in collaborating with the class for creating practice questions in future sessions. CONCLUSIONS: Developing a student-generated question bank can improve the depth and interactivity of student learning, increase session enjoyment and provide a potential resource for student assessment.
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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 | 0.035 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.012 |
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".