Fairness in Computerized Testing: Detecting Item Bias using CATSIB with Impact Present
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
In educational assessment, there is an increasing demand for tailoring assessments to individual examinees through computer adaptive tests (CAT). As such, it is particularly important toinvestigate the fairness of these adaptive testing processes, which require theinvestigation of differential item function (DIF) to yield information about itembias. The performance of CATSIB, a revision of SIBTEST to accommodate CATresponses, in detecting DIF in a multi-stage adaptive testing (MST) environmentis investigated in the present study. Specifically, the power and type I error rates on directional DIF detection of an MST environment when positive and negative impact, group ability differences, was investigated using simulation procedures. The results revealed that CATSIB performed relatively well in identifying the items with DIF when characteristics of the group and items were known. Testing companies are able to use these results to enhance test items which provide students with fair and equitable adaptive testing environments.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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