Quality assurance of item writing: During the introduction of multiple choice questions in medicine for high stakes examinations
Bibliographic record
Abstract
BACKGROUND: One Norwegian medical school introduced A-type MCQs (best one of five) to replace more traditional assessment formats (e.g. essays) in an undergraduate medical curriculum. Quality assurance criteria were introduced to measure the success of the intervention. METHOD: Data collection from the first four year-end examinations included item analysis, frequency of item writing flaws (IWF) and proportion of items testing at a higher cognitive level (K2). All examinations were reviewed before after delivery and no items were removed. RESULTS: Overall pass rates were similar to previous cohorts examined with traditional assessment formats. Across 389 items, the proportion of items with >or=5% of candidates marking two or more functioning distracters was >or=47.5%. Removal of items with high p-values (>or=85%), this item distracter proportion became >75%. With each successive year in the curriculum the proportion of K2 items used rose steadily to almost 50%. 31/389 (7%) items had IWFs. 65% items had a discriminatory power, >or=0.15. CONCLUSIONS: Five item quality criteria are recommended: (1) adherence to an in-house style, (2) item proportion testing at K2 level, (3) functioning distracter proportion, (4) overall discrimination ratio and (5) IWF frequency.
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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 | 0.060 | 0.215 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".