Efficacy of Faculty Development Training Workshops (FDTWs) on Writing High-Quality Multiple-Choice Questions at Northern Border University (NBU) in the Kingdom of Saudi Arabia (KSA)
Bibliographic record
Abstract
BACKGROUND: A multiple-choice question (MCQ) is a frequently used assessment tool in medical education for both certification and competitive examinations. Well-constructed MCQs impact the utility of the assessment and, thus, the fate of the examinee. AIMS AND OBJECTIVES: To analyze the basic science faculty perceptions of writing high-quality MCQs, to create awareness of item-writing flaws in constructing high-quality MCQs, and to determine the impact of faculty development training workshops (FDTWs) on MCQ writing skills. MATERIAL AND METHODS: An online workshop was held over two weeks for basic science faculty to learn high-quality MCQ construction. Faculty-made MCQs were analyzed for flaws, and a questionnaire assessed the impact of the workshop on MCQ construction. Pre- and post-workshop responses were compared to evaluate the necessity of such workshops for improving faculty skills in MCQ assessments. RESULTS: A total of 47 (83.2%) of participating faculty believed the workshop could reduce MCQ construction errors. The participants agreed that a series of workshops were needed for lasting improvements in MCQ construction. CONCLUSIONS: One-day short-duration workshops, such as the current one alone, cannot achieve the objectives of training participants to write high-quality MCQs. To improve student assessment through high-quality MCQs, the faculty needs to be exposed to continuous and frequent sessions that will help them.
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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 | 0.022 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".