Bibliographic record
Abstract
OBJECTIVE: The purpose of this research project was to determine if the item response theory (IRT) can successfully be applied to smaller-sized class examinations. METHODS: The Rasch mathematical model (RMM) was selected from the family of IRT models because of its ability to work with smaller sample sizes. Two simulated examinations were created for 100 students by 100-item dichotomous examinations. Examination 2 contained 20 items common with those in examination 1. Examination 1 was systematically exposed to randomly missing student responses and to entire items being removed to determine the robustness of the RMM to missing data. The two examinations were then analyzed with the RMM individually and then in combination. Student scores and IRT measures were compared to determine if the IRT could successfully place the students from the two examinations on the same metric of measure. RESULTS: The student measures were not affected when up to 20% of the student responses were randomly missing. Student measures continued to have high reliability and correlated with full matrix measures for up to 40% of items being dropped from the examination. Student scores and IRT measures correlated highly when the two examinations were combined. CONCLUSIONS: The RMM can be successfully applied to small-sized class examinations, such as those at chiropractic, medical, and other health profession institutions. It is possible to place candidates from different administrations on the same metric of measure if there is as little as a 20% overlap of items between examinations. The RMM could assist faculty in determining if differences in candidate scores are caused by ability or item difficulty.
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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 | 0.048 | 0.266 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".