Navigating the Mandate: Teachers’ Perspectives and Practices in Social-Emotional Learning in Ontario
Bibliographic record
Abstract
Social-emotional learning (SEL) is increasingly recognized as essential for student success, contributing to both academic achievement and overall well-being (Durlak et al., 2022). Teachers play a central role in implementing SEL in schools, and as such, their understanding of SEL, their training, and the degree of administrative and policy support all influence how effectively SEL is integrated into their teaching practices (Brackett et al., 2012). While there is extensive research examining SEL in schools, much of this research has been conducted in the United States, where education systems and policies differ significantly from those in Canada. This work addresses this gap by investigating the presence of SEL in Ontario’s curriculum and how Ontario teachers translate these policies into practice.A document analysis of Ontario’s curriculum documents revealed that while SEL various subject areas referenced SEL, inconsistencies in how the documents address SEL may result in it being overlooked. Further, there is a lack of explicit information regarding how to effectively integrate SEL content and the developmental trajectory of these skills. The treatment of SEL in the Ontario curriculum creates ambiguity for teachers, leaving the onus on them to develop a comprehensive approach to SEL from fragmented content. Findings from the survey of Ontario elementary teachers identified four distinct teacher dispositions toward SEL through Latent Profile Analysis (LPA). These profiles differed in their attitude towards, comfort with, commitment to training in, and perceived support from their administration for SEL. Findings indicated differences between profiles based on teachers’ years of teaching experience, teacher gender, and the amount of prior training in SEL. Further analysis revealed an association between teachers’ profile membership and their approaches to implementing SEL and using the SEL content in curriculum documents. These findings highlight a disconnect between SEL policy and practice, emphasizing the need for changes to ensure meaningful and consistent SEL implementation across Ontario. The discussion provides considerations and recommendations for policy content, policymaking, and teaching practices, focusing on supporting teachers and recognizing their agency as policy implementors.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".