Item Analysis of Multiple-Choice Question (MCQ)-Based Exam Efficiency Among Postgraduate Pediatric Medical Students: An Observational, Cross-Sectional Study From Saudi Arabia
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: Several modalities of written examination have been employed in medical education, with multiple-choice questions (MCQs) being the most frequently used and preferred format. This underlines the need to regularly assess and monitor the quality of MCQs in medical exams. Such assessment of MCQs helps to ensure that these exams are well‑designed and adequately powered to evaluate students' performance. Hence, the current study assessed the efficiency of an MCQ-based examination, in a cohort of pediatric post-graduate students in Saudi Arabia. METHODS: This observational, cross-sectional study examined the efficiency of MCQs in terms of their validity, reliability, difficulty index (DFI), discrimination index (DI), and distractor efficiency (DE). The exam consisted of a total of 48 MCQs, 144 distractors, a total score of 48, and no negative marking. RESULTS: The reliability index of 0.76 showed the consistency and reproducibility of the exam results. The exam had a DFI of 69.77%, indicating an overall moderate level of difficulty. The exam had a balanced mix of 23 easy (47.9%), 20 (41.7%) moderately difficult, and five (10.4%) tough questions. Twenty (41.6%) items had a DI of ≥0.3, indicating good discrimination of high and low performers, while the remaining 28 MCQs (58.3%) had a lower DI of ≤0.19, implying poor discriminative ability. The DE was 81.25%, indicating that the majority of distractors in the exam were functional. CONCLUSION: To the best of the author's knowledge, this is the first study among post-graduate pediatric students from Saudi Arabia, to present the results of item analysis of an MCQ-based exam. The study highlights the importance of optimizing the quality of MCQs by following established guidelines, to make MCQ-based clinical assessments more effective. It reiterates the importance of a reasonable DFI well aligned with students' knowledge levels, maximum distractor functionality, and an impactful DI, in developing high-quality MCQs.
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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 | 0.016 | 0.218 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it