Item response theory in educational assessment and evaluation
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
Item response theory provides a useful and theoretically well-founded framework for educational measurement. It supports such activities as the construction of measurement instruments, linking and equating measurements, and evaluation of test bias and differential item functioning. It further provides underpinnings for item banking and flexible test administration designs, such as multiple matrix sampling, flexi-level testing, and computerized adaptive testing. First, a concise introduction to the principles of IRT models is given. The models discussed pertain to dichotomous items (items that are scored as either correct or incorrect) and polytomous items (items with partial credit scoring, such as most types of openended questions and performance assessments). Second, it is shown how an IRT measurement model can be enhanced with a structural model, such as, for instance, an analysis of variance model, to relate data from achievement and ability tests to students’ background variables, such as socio-economic status, intelligence or cultural capital, to school variables, and to features of the schooling system. Two applications are presented. The first one pertains to equating and linking of assessments, and the second one to a combination of an IRT measurement model and a multilevel linear model useful in school effectiveness research.
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 | 0.334 | 0.326 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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