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.
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.
How this classification was reachedexpand
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 | 0.002 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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 teacher head, 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".