Elementary School ELLs' Reading Skill Profiles Using Cognitive Diagnosis Modeling: Roles of Length of Residence and Home Language Environment
Bibliographic record
Abstract
The study examined differences in reading achievement and mastery skill development among Grade‐6 students with different language background profiles, using cognitive diagnosis modeling applied to large‐scale provincial reading test performance data. Our analyses revealed that students residing in various home language environments show different reading achievement growth patterns. Earlier gaps in their reading achievement disappear the longer they reside in the target language community. Additionally, students who come from home environments where they use English and another language equally demonstrate higher skill mastery achievement levels, indicating that immigrant students' diverse home language environments do not adversely affect their reading achievement in the longer term. The study results support the evidence that multilingual home language environments are not a cause of low achievement; however, the achievement patterns of Canadian‐born English language learners (ELLs) do differ from their immigrant counterparts, revealing that time alone is not a sufficient condition of reading skill achievement. ELLs' outperformance of monolinguals after 5 years of residence is a result of ongoing instructional support and a rich linguistic environment. The study results hold important policy implications: The evaluation of ELLs' academic achievement and school effectiveness for accountability purposes should be based on longitudinal data that track their developmental growths.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".