Measures for Quality Assurance of Electronic Examinations in a Veterinary Medical Curriculum
Bibliographic record
Abstract
Since 2008, electronic examinations have been conducted at the University of Veterinary Medicine Hannover, Germany which are analyzed extensively in the current study. The aim is to assess the quality of examinations, the status quo of the electronic examination system and the implementation of recommendations regarding the conduct of exams at the TiHo. Based on the results suitable indicators for the evaluation of examinations and items as well as adequate quality assurance measures and item formats are to be identified. For this purpose, 294 electronic examinations carried out from 2008 to 2022 of the veterinary medicine course with an average of 248 participants each were evaluated with regard to the quality criteria reliability, difficulty index, and discrimination index. The main finding was that the number of items and the proportion of reused questions were identified as factors through which the quality of the examinations can be increased with simple adjustments. A higher number of items led to better reliability, whereby the required minimum reliability in examinations of 0.8 was reliably achieved from an item number of 98 questions. The proportion of reused questions should be kept low, as these had a negative influence on the characteristic values. Measures accompanying examinations, such as training of question authors and a pre- and post-review process, should also ensure the quality of examinations. For the post-review process, the distribution of examination results, reliability, item and distractor analysis are adequate indicators for evaluating examinations.
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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 | 0.066 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".