The Development Process PLC Competencies for School Administrators in Buriram Province
Bibliographic record
Abstract
The purposes of the research study were to a) study the current situation of school management and administration using professional learning community b) to develop professional learning community for the school administrators and c) follow up collaborative practice among school colleagues and administrators within school using professional learning community in Buriram Province. The researcher utilized focused interview sessions, group interview questionnaires, and Professional Learning observation within school to determine how the school colleagues perceived the implementation of professional learning communities. The results indicated that: 1) A school’s PLC isn’t working, common problems in Buriram Province include: a) Personal practice, b) Insufficient collaborative work, 3) Academics support, and 4) supportive conditions. 2) School administrators' competencies development after implementation of PLC workshop have an influence on process of using PLC in school. School administrators' competencies included supportive conditions for safe environment of PLC, creating environment that support learning collaboratively and respect for individual differences and developing collegial relationships for planning, following up of supervision process through action planning that support collaborative work among school colleagues. 3) Model of school administrators' competencies development in Buriram Province were a) a PDCA model incorporated with contemplative education that driven through small group of PLCs. This model utilized school administrators as coach and mentor to reflect on co-operation of group problem solving in the same group c) a PLC comprised of school teachers, coming together by grade level, content area, or through an interdisciplinary group. The purpose of a PLC was to build a community of teachers to focus on a common goal or objective collaboratively.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".