Integrating Social and Emotional Learning into Mathematics Education: A Multiple Case Study of JUMP Math’s Approach to Creating Socially and Emotionally Supportive Learning Environments
Bibliographic record
Abstract
Integrating social and emotional learning (SEL) into academic instruction may improve well-being and achievement. In mathematics-where anxiety and negative attitudes often hinder learning-SEL may be especially useful. This multiple case study examined how a math curriculum that explicitly embeds SEL principles shapes learning environments and teacher/student experiences. Using a multiple case study design, we conducted classroom observations, teacher interviews, and check-ins in six Grade 5-7 classrooms implementing JUMP Math, a program that centers social-emotional well-being. Three themes characterized the SEL-integrated environment: (1) Teaching Energy-steady pacing, enthusiastic delivery, and humor; (2) Learning Harmony-progressing together, peer help, and the normalization of mistakes; and (3) Emotional Stability-supportive feedback, invitations to participate, and respectful, responsive interactions. Teachers reported greater confidence and reduced math anxiety; students showed higher engagement, cooperation, and resilience in problem-solving. Findings indicate that math curricula intentionally designed with SEL can create emotionally supportive classrooms that benefit both teachers and students, while advancing academic goals. The findings contribute to understanding how academic instruction can be leveraged to develop social and emotional competence while maintaining focus on academic achievement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".