Development of English and French Language and Literacy Skills in EL1 and EL French Immersion Students in the Early Grades
Bibliographic record
Abstract
In this article, we report two studies that compared the development of English and French language and literacy skills in French immersion students identified as native English speakers (EL1s) and English learners (ELs). In study 1, 81 EL1s and 147 ELs were tested in the fall and spring terms of grade 1. The EL1s and ELs had similar outcomes and comparable gains in English phonological awareness and word reading. Comparable performance was observed on all the French measures (phonological awareness, receptive vocabulary, word reading, and reading comprehension). Only in English receptive vocabulary did the EL1s outperform the ELs. In study 2, a subset of the students in study 1 was followed into grades 2 and 3 (36 EL1s and 33 ELs). Overall, the EL1s and ELs demonstrated similar performance and gains in English phonological awareness, word reading, and reading comprehension in grades 2 and 3. The EL1s outperformed their EL peers in English expressive and receptive vocabulary across all grades, despite more rapid growth among the ELs. At the same time, outcomes on all of the French measures were comparable between the two groups. The findings of remarkably comparable performance by the EL1s and ELs suggest that overall, French immersion does not impede English language and literacy skill development in ELs, making it a viable option for these students.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".