Whole School Approaches to Supporting English Language Learners in Public Elementary Schools
Bibliographic record
Abstract
The purpose of this qualitative research study was to discover more about what a whole school approach to supporting English language learners entails and prioritizes. The main research question that guided this study was: how is one Toronto school effectively enacting a whole school commitment and approach to supporting English Language Learners? Data was collected through semi-structured interviews with a classroom teacher, ESL support teacher, and a school principal, all working within the same TDSB school. Findings suggest that it is essential to create inclusive, caring, and risk-free school-wide environments for ELLs to succeed. Having professionals within the school who create personal connections with ELLs and who can relate to them on a personal level was a large factor in supporting this approach. Another finding was the importance of communication and collaboration between key stakeholders in facilitating a whole school approach. These stakeholders include a variety of individuals both within the school and the surrounding community. A key stakeholder was found to be the school board as it provides the resources and finances that the school needs in order to implement a whole school approach. An implication of these findings is the important of understanding that the school board does not actually support or implement the whole school approach, but provides resources that the school can choose to use to create this type of approach to supporting ELLs.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".