British Columbia Teachers’ Beliefs and Experiences on Implementing Social and Emotional Learning
Bibliographic record
Abstract
Social and emotional learning (SEL) is a school-based educational intervention aimed at helping children and youth develop competencies to promote positive academic growth, behavior, and development. The success of the SEL intervention depends on several factors beyond the curriculum, including the role of teachers, the quality of the SEL implementation, and the classroom and school context. While research on the teachers’ role in successful SEL implementation has been conducted, less is known about how SEL is implemented in an impoverished and diverse school context in British Columbia in Northern Canada. The purpose of this generic qualitative study was to explore Northern British Columbia elementary school teachers’ experiences and beliefs about implementing SEL practices and programs in their school and classroom. The conceptual framework for this study was based on the collaboration for academic, social, and emotional learning conceptualization of SEL and on Schonert-Reichl’s three-component SEL framework. Semistructured interviews with 12 elementary school teachers who had experience implementing SEL constituted the data for this study. Thematic analysis was used to analyze the content of the interviews. The results of this study underscored the significance of SEL in promoting student well-being and academic success, the role of ongoing professional development for educators, and the necessity for collaborative efforts to overcome implementation hurdles. The findings of this study have the potential to be used for positive social change in the creation and implementation of best practices for SEL and in providing a better understanding of how teachers juggle implementing SEL in the context of the reality of their diverse student population. British
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".