Using Home Language as a Pedagogical Resource: Working Collaboratively with Ontario Educators to Support English Language Learners in the Classroom
Bibliographic record
Abstract
Coherent policies to address the implications of linguistic diversity for instruction are lacking at all levels of schooling in Canada (Cummins, 2014; Volante et al., 2020). Many English language learners (ELLs)—often refugees or those of lower socioeconomic status—experience academic difficulties (Volante et al., 2017). Teachers report low self-efficacy and a lack of preparedness to meet the professional challenges of continually rising numbers of ELLs in their classrooms (Faez, 2012). Numerous studies demonstrate that students who have the opportunity to maintain and develop their Home Language (L1) at school outperform their peers in English-only programs, achieve better academic outcomes, and experience lower drop-out rates (Baker, 2011; Genesee et al., 2006; Thomas & Collier, 2002). This is because the academic language and literacy skills that students acquire in their L1 readily transfer to English (Cummins, 2017). Additionally, when L1 is validated as a valuable resource for learning, students experience an affirmation of self that contributes to positive identity formation. By joining forces with educators in collaborative professional learning teams, this study connects what we know (an extensive knowledge base that argues that the use of students’ L1 is essential to their success) and what we do (instructional practice that predominantly excludes students’ L1) in the classroom. It asks: in what ways and to what extent can collaborative professional development assist educators in providing more equitable educational opportunities for English language learners in Ontario schools?
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".