Change in home language environment and English literacy achievement over time: A multi-group latent growth curve modeling investigation
Bibliographic record
Abstract
In most studies investigating the educational outcomes of linguistically diverse students, variables that identify this population have been considered as static. In reality, owing to the dynamic nature of students and their families, students’ home language environments change over time. This study aims to understand how elementary school students’ home language environments change over time, and how longitudinal patterns of English literacy achievement across grades 3, 6, and 10 differ among students with various home language shift patterns in Ontario, Canada. The longitudinal cohort data of 89,609 students between grades 3 and 10 from the provincial assessments were analyzed for changes in their home language environment. A subsample of 18,000 students was used to examine different patterns of relative literacy performance over time and their associations with immigration background and early intervention programming using multi-group latent growth curve modeling. Our findings suggest a strong movement toward an English-dominant home language environment among multilingual students; yet, students whose homes remained as multilingual demonstrated the highest literacy achievement in the early grade as well as the highest improvement in relative performance over time. The paper draws implications for promoting students’ home language, instilling a positive view of multilingual competence.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".