The Effect of Linguistic Factors on Assessment of English Language Learners’ Mathematical Ability: A Differential Item Functioning Analysis
Why this work is in the frame
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Bibliographic record
Abstract
Increasing linguistic diversity in classrooms has led researchers to examine the validity and fairness of standardized achievement tests, specifically concerning whether test score interpretations are free of bias and score use is fair for all students. This study examined whether mathematics achievement test items that contain complex language function differently between two language subgroups: native English speakers (EL1, n= 1 000), and English language learners (ELL, n= 1 000). Confirmatory Differential Item Functioning (DIF) analyses using a SIBTEST were performed on 28 mathematics assessment items. Eleven items were identified to have complex language features, and DIF analyses revealed that seven of these items (63%) favored EL1s over ELLs. Effect sizes were moderate (0.05 ≤βˆuni<0.10) for six items, and marginal (βˆuni<0.05) for one item. This paper discusses validity issues with math achievement test items assessing ELLs and calls for careful test development and instructional accommodation in the classroom.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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