Effectiveness of e-Learning in a Medical School 2.0 Model: Comparison of Item Analysis for Student-Generated vs. Faculty-Generated Multiple-Choice Questions.
Bibliographic record
Abstract
BACKGROUND: Early reports in the literature describe using student-generated questions as a method of student learning as well as augmenting question exam banks. Reports on the performance of student-generated questions versus faculty-generated questions, however, remain limited. This study aims to compare the question performance of student-generated versus faculty-generated multiple-choice questions (MCQ). OBJECTIVES: To determine if student-generated questions using mobile audience response systems and online discussion boards have similar item discrimination scores as faculty-generated questions. METHODS: A team-based learning session was used to create 113 student-generated multiple-choice questions (SGQs). A 20 question MCQ quiz was presented to a second year medical school class made of 10 randomly selected SGQs and 10 randomly selected faculty-generated multiple-choice questions (FGQs). Item analysis was performed on the test results. RESULTS: The data showed no statistical difference in the point-biserial scores between the two groups (average point-biserial 0.31 students vs 0.36 faculty, p=0.14), with 90% of student-generated and 100% of faculty-generated questions meeting a cut-off of point-biserial score >0.2. Interestingly, student-generated questions were statistically more difficult than the faculty-generated questions (Item Difficulty score 0.46 students vs 0.69 faculty, p=0.003). CONCLUSIONS: This study suggests that student-generated compared to faculty-generated MCQs have similar item discrimination scores, but are perhaps more difficult questions.
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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 | 0.030 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".