Understanding Teachers' Perceptions and Practices: Exploring the Integration of Social-Emotional Learning in Mathematics Education
Bibliographic record
Abstract
This study investigates the integration of Social-Emotional Learning (SEL) into the Ontario Elementary Mathematics Curriculum, utilizing the CASEL framework (CASEL, n.d) to analyze teachers' experiences. The CASEL framework offers a holistic perspective to fostering the social and emotional development of individuals, employing a model with five core competencies: social awareness, self-awareness, self-management, responsible decision-making, and relationship skills. Using a qualitative research methodology with surveys and interviews as data instruments and employing Creswell and Guetterman’s (2018) approach to data analysis, the research explores teachers’ perceptions and classroom practices in implementing SEL, as well as the successes and challenges educators face during this process. It also examines how various forms of support and resources impact the effectiveness of SEL integration. Findings reveal that while some teachers experience strong administrative support, others face challenges with inadequate professional development (PD) and limited SEL-specific resources. Comprehensive PD, tailored SEL curriculum, and collaboration opportunities are crucial elements for enhancing SEL practices. Administrative support plays a key role in fostering a school-wide commitment to SEL. Future research directions include evaluating different PD models, exploring SEL integration across various subjects and contexts, investigating long-term effects on student outcomes, and addressing resistance to SEL among students.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".