Bibliographic record
Abstract
This study aimed to examine the effect of using items from previous exams on students’ pass-fail rates and on the psychometric properties of the tests and items. The study included data from 115 tests and 11,500 items used in the midterm and final exams of 3,910 students in the preclinical term at the Faculty of Medicine from 2014 to 2019. Data were analyzed using descriptive statistics related to the total test scores, item difficulty and item discrimination values, and internal consistency values for reliability. The Shapiro-Wilks test was used to evaluate the distribution structure, and t test were used to analyze the differences between groups. The findings showed that the mean item repetition rate from 2014 to 2019 ranged from 16.98% to 39.00%. The total score variance decreased significantly as the percentage of test items increased. There was a significant, moderately positive relationship between the percentage of repeated test items and the number of students eligible to pass their grades. Item difficulty values obtained from initial item use were significantly lower than those obtained from repeated item use. We conclude that test items and answer keys should not be published by test makers unless they have the means such as the infrastructure, budget, and personnel to develop new items in place of the ones previously published in test banks.
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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 | 0.070 | 0.506 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".