The Effect of Linguistic Factors on Assessment of English Language Learners’ Mathematical Ability: A Differential Item Functioning Analysis
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.
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.
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 | 0.024 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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 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".