I Can Succeed for Preschools: A Randomized Control Trial of a New Social-emotional Learning Program
Bibliographic record
Abstract
The current randomized control trial evaluated the effectiveness of a new social–emotional learning (SEL) program, I Can Succeed for Preschools (ICS–PS). ICS–PS aims to improve preschoolers’ social/interpersonal, emotional, and academic skills in an integrative way, focusing on children’s core executive functions (EFs). The various components of the program are delivered sequentially throughout the school year via stories, physical activity, games, and role–playing. Six preschools were randomly assigned to either intervention (N = 49, mean age 66.37 months, 39% boys and 61% girls) or treatment as usual (TAU; N = 43, mean age 64.28 months, 56% boys and 44% girls). ICS–PS was delivered from November 2016 until May 2017. EF, emotional competence, social skills, behavior problems, and academic functioning were measured before and after the intervention. Research Findings: Only children in the intervention group significantly improved in their EFs, receptive emotion knowledge, and emergent literacy, as well as in reducing internalizing problems. No change was found in social skills, indicating the need for more direct and explicit social skills training throughout the year. Practice or Policy: These results, along with high treatment fidelity and program satisfaction, suggest that ICS–PS is a feasible and effective program for preschoolers. The research highlights the importance of prevention in early childhood.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".