Supporting ELLs: Ontario Elementary Teachers' Experiences Using CRRP
Bibliographic record
Abstract
The aim of this qualitative research study is to understand how Ontario elementary teachers support English Language Learners through the use of culturally relevant and responsive pedagogical practices (CRRP). Although existing literature on CRRP speaks to what the approach entails, there is little research on how teachers engage in this approach in the classroom and the results they observe for their ELL and non-ELL students. This research project intervenes this gap by highlighting how a small sample of elementary school teachers enact CRRP to support their diverse learners, the challenges they face as a result, and the actions they take to overcome these challenges. This study is guided by the main research question of: How is a sample of elementary school teachers enacting culturally relevant teaching to support their ELL students? Findings from the study suggest that teachers who engage in a CRRP approach create inclusive classrooms that connect instruction to students' interests, incorporate students' culture and L1 through the use of visuals, and allow students' to draw upon their L1 through oral and written engagement. Despite these outcomes, findings suggest that more needs to be done to better prepare teachers to support their diverse learners. As well, ministries of education must allocate more money for funding and training for pre and in-service teachers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".