Differential Functioning of Reading Subskills on the OSSLT for L1 and ELL Students: A Multidimensionality Model‐Based DBF/DIF Approach
Bibliographic record
Abstract
The increasing numbers of English language learners (ELLs) in Canadian schools pose a significant challenge to the standards‐based provincial tests used to measure proficiency levels of all students from various linguistic and cultural backgrounds. This study investigated the extent to which reading item bundles or items on the Ontario Secondary School Literacy Test (OSSLT) function differentially for Grade 10 students who speak only or mostly English at home (first language [L1] students; n = 1,969) and those whose home language is something other than English (ELL students; n = 3,675). Based on Roussos and Stout's (1996a) multidimensionality‐based DIF analysis paradigm, a variety of substantive and statistical techniques were employed: (a) content review by English as a second language (ESL) experts, (b) exploratory and confirmatory dimensionality analyses, and (c) confirmatory differential bundle functioning (DBF)/differential item functioning (DIF) procedures. The evidence gathered in the study indicated that items associated with vocabulary knowledge favored L1 students, whereas items requiring grammatical knowledge or integrated reading and writing skill favored ELL students. Instructional implications for the promotion of effective literacy education programs are discussed, as is the development of a literacy curriculum that can meet the needs of linguistically diverse learners in a multilingual context.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".