Re-using questions in classroom-based assessment: An exploratory study at the undergraduate medical education level
Bibliographic record
Abstract
INTRODUCTION: To alleviate some of the burden associated with the development of novel quality questions on a regular basis, medical education programs may favour the use of item banks. This practice answers the real pragmatic need of having to create exams de novo at each administration while benefiting from using psychometrically sound questions to assess students. Unfortunately, programs cannot prevent trainees from engaging in cheating behaviours such as content sharing, and little is known about the impact of re-using items. METHODS: We conducted an exploratory descriptive study to assess the effect of repeated use of banked items within an in-house assessment context. The difficulty and discrimination coefficients for the 16-unit exams of the past 5 years (1,629 questions) were analyzed using repeated measure ANOVAs. RESULTS: Difficulty coefficients increased significantly (M = 79.8% for the first use of an item, to a mean difficulty coefficient of 85.2% for the fourth use) and discrimination coefficients decreased significantly with repeated uses (M = 0.17, 0.16, 0.14, 0.14 for the first, second, third and fourth uses respectively). DISCUSSION: The results from our study suggest that using an item three times or more within a short time span may cause a significant risk to its psychometric properties and consequently to the quality of the examination. Pooling items from different institutions or the recourse to automatic generated items could offer a greater pool of questions to administrators and faculty members while limiting the re-use of questions within a short time span.
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 | 0.005 | 0.010 |
| 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) | 0.002 | 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".