Towards play-based curriculum: moderating effect of early childhood teacher’s experience on the relationship between teacher’s playfulness, teaching efficacy, and classroom interaction during curriculum revision
Bibliographic record
Abstract
In 2019, South Korea revised its national level early childhood curriculum emphasizing child-centered, play-based approach. The revision process involved training for 24 pilot preschools and 20 childcare centers. This study compared participants based on participation in the pilot program to determine effectiveness of the program. A total of 259 early childhood teachers completed an online survey. Among the participants, 105 participated in the pilot program. Using SPSS 27.0, descriptive statistical analysis, independent sample t-tests, and bivariate correlations were calculated. PROCESS macro Model 7 was utilized to analysis the moderating effect of the experience on the relationship between teachers’ playfulness, teaching efficacy, and classroom interaction. According to the analysis, teachers with more years of experience tended to have higher levels of teaching efficacy and classroom interaction. Also, teacher’s playfulness significantly influenced classroom interaction mediated by teaching efficacy. Lastly, the moderating effect of experience was statistically significant only for teachers who participated in the pilot program. Teachers with low to mid-levels of experience displayed a significant moderating effect on teaching efficacy. The revision of the curriculum poses a new challenge for teachers. Therefore, it is importance to tailor curriculum revisions and teacher training programs according to teachers’ levels of experience.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".