The Effects of MMCI Course on Chinese Preschool Teachers’ Child-Centered Beliefs
Bibliographic record
Abstract
In the context of China’s preschool education reform, child-centered education is widely advocated, yet adult-centered tendencies remain visible in daily practice. This study examined whether the Making the Most of Classroom Interactions (MMCI) program can enhance in-service kindergarten teachers’ child-centred beliefs. Using a quasi-experimental design, 48 teachers from Jinan were assigned to an MMCI group or a control group. The MMCI group completed a six-week, practice-integrated course with weekly training followed by immediate classroom application; implementation was monitored via weekly video uploads from both groups. Beliefs were measured pre- and post-intervention (higher scores = more adult-centred). The results of independent samples t-test indicated that there was no significant difference (p=.590>.05) in the post-test scores between two grous. ANCOVA controlling pre-test, education, and years of service likewise showed no group effect ( p=.587>.05), while baseline beliefs strongly predicted outcomes ( p< .001). These findings suggest that short-term MMCI, even when integrated with practice, may be insufficient to shift entrenched beliefs; future work should lengthen duration, localize MMCI via context-relevant case analysis and reflective discussion of key child-centred practices, and employ adequately powered multi-region randomized designs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".