A Process for Reviewing and Evaluating Generated 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
Testing organization needs large numbers of high‐quality items due to the proliferation of alternative test administration methods and modern test designs. But the current demand for items far exceeds the supply. Test items, as they are currently written, evoke a process that is both time‐consuming and expensive because each item is written, edited, and reviewed by a subject‐matter expert. One promising approach that may address this challenge is with automatic item generation. Automatic item generation combines cognitive and psychometric modeling practices to guide the production of items that are generated with the aid of computer technology. The purpose of this study is to describe and illustrate a process that can be used to review and evaluate the quality of the generated item by focusing on the content and logic specified within the item generation procedure. We illustrate our process using an item development example from mathematics drawn from the Common Core State Standards and from surgical education drawn from the health sciences domain.
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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it