Developing a Taxonomy of Item Model Types to Promote Assessment Engineering
Bibliographic record
Abstract
An item model serves as an explicit representation of the variables in an assessment task. An item model includes the stem, options, and auxiliary information. The stem is the part of an item which formulates context, content, and/or the question the examinee is required to answer. The options contain the alternative answers with one correct option and one or more incorrect options or distractors. The auxiliary information includes any additional material, in either the stem or option, required to generate an item, including texts, images, tables, and/or diagrams. In this study, we first present a taxonomy for item model development where variables in the stem are crossed with variables in the options to create a matrix of possible item model types. We then provide examples of each stem-by-option combination. Finally, we develop a software engine and apply the software to each item model type to generate multiple instances for each model.
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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 | 0.050 | 0.139 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".