The impact of systematically repairing multiple choice questions with low discrimination on assessment reliability: an interrupted time series analysis
Bibliographic record
Abstract
At our centre, we introduced a continuous quality improvement (CQI) initiative during academic year 2018-19 targeting for repair multiple choice question (MCQ) items with discrimination index (D) < 0.1. The purpose of this study was to assess the impact of this initiative on reliability/internal consistency of our assessments. Our participants were medical students during academic years 2015-16 to 2020-21 and our data were summative MCQ assessments during this time. Since the goal was to systematically review and improve summative assessments in our undergraduate program on an ongoing basis, we used interrupted time series analysis to assess the impact on reliability. Between 2015-16 and 2017-18 there was a significant negative trend in the mean alpha coefficient for MCQ exams (regression coefficient -0.027 [-0.008, -0.047], p = 0.024). In the academic year following the introduction of our initiative (2018-19) there was a significant increase in the mean alpha coefficient (regression coefficient 0.113 [0.063, 0.163], p = 0.010) which was then followed by a significant positive post-intervention trend (regression coefficient 0.056 [0.037, 0.075], p = 0.006). In conclusion, our CQI intervention resulted in an immediate and progressive improvement reliability of our MCQ assessments.
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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 | 0.072 | 0.235 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".