A Critical Exploration of the Integration of ELLs' L1 in the Classroom
Bibliographic record
Abstract
A great percentage of schools in the Greater Toronto Area (GTA) are composed of English Language Learners (ELLs) from all over the world. This research paper explores the effects of integration ELLs’ first language (L1) in both ELL classrooms and in mainstream classrooms. This study examines the following research question: What are the effects of integrating ELLs’ L1 in the classroom? The data for this study was collected through interviews with two participants. The two participants are current teachers in the York Region District School Board (YRDSB) who have experience integrating ELLs’ L1 in their classrooms. The findings suggest that educators positively view the integration of ELLs’ L1 and believe that it is most effective when students are at stage 1. However, another finding suggests that educators are faced with a challenge when integrating ELLs’ L1 as they need to seek and apply appropriate tools and resources to best support their students in their L1.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".