Re-imagining professional development for social and emotional learning : a case study
Bibliographic record
Abstract
This single descriptive case study explores how ongoing and collaborative inquiry-based professional development supports educators as they explore, develop, and apply practices that promote social and emotional learning (SEL) beyond the typical conventions of SEL programs. Educators from a large suburban school district in British Columbia, Canada, participated in a professional development initiative that spanned the 20192020 school year. Together, the teachers examined how they could provide students with opportunities to practice and apply SEL skills within classroom routines, academic instruction, and learning activities. The research study was designed to investigate the following questions: (1) How do educators enact SEL-promoting practices in their classrooms? (2) How does engaging in collaborative inquiry-based professional development support educators in their efforts to explore, develop, and apply SEL-promoting practices beyond the typical conventions of SEL programs? (3) How did COVID-19 impact teachers’ SEL practice? (The third question was developed in response to the unprecedented and unexpected context created by the COVID-19 pandemic.) Findings from this study suggest the importance of educators learning together in a community in which they can ideate, share stories, exchange resources, engage in reflective dialogue, plan instruction, and apply their learning. Results underscore the importance of attending to the relational elements of learning for both teachers and students, including co-creating learning environments that attend to learners’ emotions, foster trust, and promote academic risk taking. Implications for the role of ongoing and collaborative professional development and SEL-supportive practices are discussed.
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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 | 0.013 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.015 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".