Social-Emotional Learning and Evaluation in After-School Care: A Working Model
Bibliographic record
Abstract
Social-emotional competence in children is an important area in which to develop and improve effective programs and evaluation. Research shows a positive association between social-emotional learning (SEL) and improvements in students’ conduct, social behavior, and school engagement as well as decreases in high-risk behaviors such as taking drugs, smoking and aggression. Extensive research points to the positive benefits of successful SEL curriculum in schools, but less research exists on SEL implementation in after-school care settings. Since social-emotional competence is correlated with higher positive effects and a decrease of negative effects in the social, behavioral, and academic outcomes of children exposed to these programs, more research is needed on the most effective format and environment for implementation. The purpose of this article is to review this research, and report the results of an evaluation comparing pre- and post-program survey data from children (n = 125; age range=4-11 years) attending an after-school program that has incorporated an SEL curriculum. Results showed significant increases in two SEL competencies: empathy and self-soothing. The advantages to providing both SEL instruction and evaluation in after-school care settings in addition to schools is also explored.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".