Cultivating young minds: Exploring the relationship between child socio-emotional competence, early childhood education and care quality, creativity and self-directed learning
Bibliographic record
Abstract
Creativity and self-directed learning (SDL) have been identified as two key skills that children need to develop for success in the 21st century. As such, developing such skills has become a priority. High quality early childhood education and care (ECEC) can play a pivotal role in supporting a myriad of children's developmental outcomes. This study uses multi-level modeling on 484 children between 30 and 75 months of age (Mean age = 48) across 87 ECEC classrooms in Toronto. We tested associations between two skills (creativity and SDL), children's social and emotional competence (hyperactivity, prosocial behaviour, and emotional problems) and ECEC quality (professional development, ratios and emotional support). We found significant relationships between children's social and emotional outcomes and their creativity skills, but not their SDL. In addition, the proportion of professional development opportunities conducted onsite was positively associated with children's creativity. Implications for future research, policy and practice are discussed. This study contributes to the understanding of how to support two skills needed in the wake of the fourth industrial revolution. First, we examine the relationship between children's social and emotional development and their creativity and self-directed learning. We then examine the role played by the quality of their early childhood education and care (ECEC) settings as contexts to learn these important skills when accounting for their children's social and emotional development. Finally, we test the robustness of these associations by controlling for important contextual factors (age, months in the program, family income, and maternal education).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".