What is Social Emotional Learning? Clarifying the notion through a literature review and an empirical study with educators.
Bibliographic record
Abstract
Social emotional learning (SEL) is the predominant approach to supporting students’ non-academic development in schools. The perceived mental health crisis of Canadian youth has brought SEL programming to the forefront as a school-based solution. Despite this, debates over the effectiveness and what exactly is meant by the term SEL demands a clarification of SEL and its appropriateness for Canadian schools. Therefore, this thesis aims to address the ambiguities of this notion known as SEL, and to help educators better understand SEL to inform decision-making on the integration of SEL into Canadian schools. To meet these aims, this thesis presents two manuscripts. The first manuscript provides a critical review of the literature on SEL that offers a nuanced understanding of SEL’s appropriateness for Alberta students. The review concludes that the heterogeneity of SEL programs, historical development, and current research on the effectiveness of SEL demonstrates how it cannot be claimed that SEL is an evidence informed approach appropriate for Alberta schools pursuing policy of multiculturalism in education (Government of Canada, 1988). The second manuscript presents a thematic analysis of ten interviews with educators on their perspectives towards the value and meaning of SEL. Two themes have emerged from educators’ perceptions of SEL. The first is a tension between two notions of SEL, one accepted as embedded in their existing teaching roles, the other rejected as additional responsibilities that educators are unprepared to fulfil. The second theme points to a challenge perceived by educators that has not been documented by the existing literature. That is, how to appropriately navigate personal connections with students when fostering SEL? The important implication of this research is that future SEL programming in Canadian schools should aim at reconciling the two notions of SEL; the development and implementation of such programs need to incorporate educators’ existing expertise with SEL. Further, there exist gaps in educators’ understanding and knowledge of SEL, which can be addressed by professional training that introduces research and theory on SEL. To sum, SEL programs should empower educators by allowing them to choose how to teach SEL in a way appropriate for the specific cultural and political contexts in which their classroom is embedded.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| 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".