Weaving Together Social-Emotional Learning and Self-Regulated Learning: Research and Practice
Bibliographic record
Abstract
Abstract In recent years, the fields of Social and Emotional Learning (SEL) and Self-Regulated Learning (SRL) have gained particular traction as educators aim to promote 21 st century skills. SEL is the process through which individuals develop and maintain the ability to recognize and regulate emotions, set and achieve goals, build healthy relationships, and handle interpersonal situations. SRL is a cyclical process that refers to the ability to manage one’s thoughts, emotions, and behaviours in the service of goals and environment demands. Both fields are known to support learners’ social, emotional, cognitive and metacognitive development, yet little research has explored the complementary nature of the two fields. This paper builds from a comprehensive, systematic literature review of SEL and SRL to locate synergies between their theoretical underpinnings, related conceptual models, and classroom application. The paper highlights a practical framework for embedding SEL and SRL promoting practices directly into classroom practice throughenvironmental supports, teacher modeling, academic integration, activity design, and pedagogical approaches. It is abundantly clear that, when taken together, SEL and SRL have the capacity to enrich teaching and learning practices that might increase students’ potential for learning and development and would benefit the education community.
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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 | 0.044 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".