Using automatic item generation to create multiple‐choice test items
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Many tests of medical knowledge, from the undergraduate level to the level of certification and licensure, contain multiple-choice items. Although these are efficient in measuring examinees' knowledge and skills across diverse content areas, multiple-choice items are time-consuming and expensive to create. Changes in student assessment brought about by new forms of computer-based testing have created the demand for large numbers of multiple-choice items. Our current approaches to item development cannot meet this demand. METHODS: We present a methodology for developing multiple-choice items based on automatic item generation (AIG) concepts and procedures. We describe a three-stage approach to AIG and we illustrate this approach by generating multiple-choice items for a medical licensure test in the content area of surgery. RESULTS: To generate multiple-choice items, our method requires a three-stage process. Firstly, a cognitive model is created by content specialists. Secondly, item models are developed using the content from the cognitive model. Thirdly, items are generated from the item models using computer software. Using this methodology, we generated 1248 multiple-choice items from one item model. CONCLUSIONS: Automatic item generation is a process that involves using models to generate items using computer technology. With our method, content specialists identify and structure the content for the test items, and computer technology systematically combines the content to generate new test items. By combining these outcomes, items can be generated automatically.
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.
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 | 0.008 | 0.722 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it