Quality in Chinese Preschool Classrooms: Its Structural Influencing Factors and Associations with Child Development
Bibliographic record
Abstract
Research Findings: Process quality, reflecting teacher instructional methodologies and teacher-child interactional quality, directly impacts child outcomes and has become a focal point as early childhood education and care (ECEC) programs expand globally. This expansion underscores the urgency of establishing suitable structural and professional standards. To contribute to this discourse, this study aims to investigate the structural influencing factors of process quality and its associations with child outcomes. Ninety-six classrooms (202 teachers) were observed to measure their classroom process quality using ECERS-E and SSTEW observation scales, while 547 children were evaluated on emergent literacy, numeracy, and social-emotional skills. Multilevel modeling revealed that the child-to-teacher ratio, the preschool type (private vs. public), and the preschool classification (district, city, province-level) were significant influencing factors of process quality. Science and diversity instruction, and critical thinking support were associated with children’s academic and social-emotional development. Practice or Policy: The associations between different aspects of process quality on child development outcomes provide valuable guidance for targeted professional development on science, diversity, and the support of critical thinking in preschools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".