Teacher Efficacy for Teaching in Multilingual School Contexts in Ontario
Bibliographic record
Abstract
This study is designed to understand teacher learning and efficacy for teaching in Ontario’s linguistically, culturally and racially diverse classrooms. The knowledge gained from this study can be used to promote quality culturally and linguistically inclusive pedagogy in Canada’s multilingual schools. Over 20% of Canada’s population is foreign-born and recent reports claim that by 2036 more than 30% of the population would have a mother tongue other than English (Statistics Canada, 2017). Minority students include Canadian born learners (from Indigenous and immigrant families), foreign-born immigrant students, and refugees. These students succeed in academics when teachers make instruction relevant throughout the curriculum (Cummins & Early, 2015). Teacher efficacy beliefs (Bandura, 1997), or teachers’ confidence in their ability to perform specific tasks, is a way of understanding teachers’ pedagogical capabilities. However, there is a gap in knowing how efficacious teachers are to support minority students and how teacher education programs help teachers develop skills and knowledge for multilingual students. The study will address this notable gap by measuring teacher efficacy to teach in multilingual school contexts, exploring levels of efficacy of novice and experienced teachers to teach in such contexts, and identifying ways in which teachers incorporate culturally and linguistically inclusive pedagogy in elementary and secondary classrooms.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".