Effectiveness of e-Learning in a Medical School 2.0 Model: Comparison of Item Analysis for Student-Generated vs. Faculty-Generated Multiple-Choice Questions.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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