What Makes Young Children Happy? Exploring Well-Being Through Children’s Perspectives in Early Childhood Education
Bibliographic record
Abstract
Early Childhood Education and Care (ECEC) environments play a crucial role in fostering young children’s well-being, yet limited research has explored well-being from children’s perspectives, especially in countries outside of WEIRD (Western, Educated, Industrialized, Rich, and Democratic) contexts. This study aims to fill this gap by capturing the experiences of 316 children (ages 4–7 years) from public and private kindergartens in Kazakhstan using the Draw, Write, and Tell (DWT) technique. Thematic analysis, guided by the PERMA-H framework, identified key dimensions shaping children’s well-being, including Positive Emotions (e.g., nature, play, toys), Engagement (e.g., imaginative play, artistic activities, learning), and Relationships (e.g., peers, teachers, family). While Meaning, Accomplishment, and Health were mentioned less frequently, they provided insight into how children derive well-being from celebrations, achievements, and physical activities. The findings suggest that the PERMA-H model usefully translates the broad concept of well-being into tangible and educationally meaningful dimensions. However, it does not fully align with young children’s lived realities that favour the “here-and-now” as well as the joyful, engaging, and socially supportive experiences as central to their well-being. The study contributes to the global discourse on early childhood well-being by amplifying young children’s voices from a non-WEIRD context. We present contextually grounded insights for educators and policymakers to enhance children’s positive experiences and holistic development in early learning settings.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".