Kindergarten teachers’ facilitation of social and emotional learning in classroom play contexts
Bibliographic record
Abstract
Play occupies a critical role in the kindergarten classroom, and the expansion of formal play-based learning programmes have brought connections between play and learning to the forefront. With respect to social and emotional learning (SEL), child-directed play has been viewed as critical, while teacher direction has been framed as a potential disruption to this learning. However, teachers facilitate different types of play in kindergarten with varying degrees of assistance, yet few studies have looked at the ways teachers target children’s SEL across different play configurations. The current study gathered observational data in 20 play-based kindergarten classrooms. Video data were coded according to the type of play (child-directed or teacher-facilitated), SEL competency, and type of teacher promotion. Across both types of play, teachers focused primarily on supporting children’s relationship skills and self-management skills in an incidental way, while other competencies not practiced spontaneously in play largely went overlooked. Skills were targeted most often during teacher-facilitated play, underscoring the importance of this context for children’s SEL. Overall, these findings illustrate positive examples of teacher promotion of SEL in play, while underscoring the need for more intentional targeting of broader competencies to promote all areas of children’s SEL in a developmentally appropriate manner.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".