Innovative Leadership Factors and Leader Characteristics that Affecting Professional Learning Community of Primary Schools in Bangkok and Its Vicinity
Bibliographic record
Abstract
This research aimed to investigate the innovative leadership factors and leader characteristics of school administrators in affecting teachers’ involvement in the professional learning community of primary education schools in Bangkok and its vicinity of Thailand. Hence, the researcher would shed light on a linear structural relationship model to examine the impacts of innovative leadership factors and leader characteristics of primary school administrators on teachers’ involvement in the professional learning community. A quantitative approach survey design was employed in this research. A total of 840 respondents responded to questionnaires in a proportional of two teachers to one school administrator from 280 primary schools. The respondents participated in a survey utilizing a multi-stage sampling technique. The researcher planned to test whether the identified innovative leadership factors and leader characteristics are fitting with empirical data as the key research output. The findings indicated that there was a total of five innovative leadership factors and three leader characteristics in a professional learning community model. The linear structural relationship model was supported to the empirical data, with χ2 = 42.321, df = 31, χ2 /df = 1.3652, CFI = 0.998, TLI = 0.997, RMSEA = 0.021, and SRMR = 0.01, p = 0.0845. In conclusion, the linear structural relationship model for primary school administrators has a goodness of fit with the attained data. Finally, the findings of this research have successfully proposed a linear structural relationship model that would be guidelines for a primary school administrator to develop his capabilities to promote a professional learning community.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".