Bibliographic record
Abstract
Application of computerized adaptive testing (CAT) in medical education is still spare in the high stakes examination or in the school-based examination. In the medical school in Belgium, CAT was used for an assessment tool in general practice as pilot test was reported. In Hallym University, CAT has been introduced in the evaluation of the students' performance as in-course general evaluation test and parasitology test. Another examples of application of CAT for high stakes examination are Medical Council of Canada Qualifying Examination - Part 1 in Canada and National Council Licensure EXamination - Registered Nurse in USA. CAT has some merits such as accurate estimation of the ability parameters of the examinees and the shorter period of examination. To apply the CAT in medical education more actively, medical teachers should have an interest in the modern measurement theories such as item response theory and technologies. It is still uncertain if CAT may be prosperous in the medical education as a tool for the measurement of the examinees' ability. However, we should prepare the era of application of CAT in high stakes examination such as medical licensing examination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".