Innovative Leadership Factors and Leader Characteristics that Affecting Professional Learning Community of Primary Schools in Bangkok and Its Vicinity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it