Medical school 2.0: How we developed a student-generated question bank using small group learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it