Three Modeling Applications to Promote Automatic Item Generation for Examinations in Dentistry
Bibliographic record
Abstract
Test items created for dentistry examinations are often individually written by content experts. This approach to item development is expensive because it requires the time and effort of many content experts but yields relatively few items. The aim of this study was to describe and illustrate how items can be generated using a systematic approach. Automatic item generation (AIG) is an alternative method that allows a small number of content experts to produce large numbers of items by integrating their domain expertise with computer technology. This article describes and illustrates how three modeling approaches to item content-item cloning, cognitive modeling, and image-anchored modeling-can be used to generate large numbers of multiple-choice test items for examinations in dentistry. Test items can be generated by combining the expertise of two content specialists with technology supported by AIG. A total of 5,467 new items were created during this study. From substitution of item content, to modeling appropriate responses based upon a cognitive model of correct responses, to generating items linked to specific graphical findings, AIG has the potential for meeting increasing demands for test items. Further, the methods described in this study can be generalized and applied to many other item types. Future research applications for AIG in dental education are discussed.
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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 | 0.016 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".