Needs Assessment of Participatory School Management for Academic Effectiveness in Provincial Administrative Organization Schools: A Study of Northeast Thailand
Bibliographic record
Abstract
This research examined the needs assessment and development guidelines for participatory school management to enhance academic effectiveness in Provincial Administrative Organization (PAO) schools across Northeast Thailand. The study employed a mixed-methods design conducted in two phases. The first phase involved document synthesis and survey analysis of 383 participants (275 administrators/teachers and 108 school board members) selected through stratified random sampling from 216 PAO schools. The second phase comprised qualitative interviews with five educational experts chosen through purposive sampling. Research instruments included semi-structured interviews, 5-point rating scale questionnaires (reliability coefficients: 0.942–0.980), and guideline evaluation forms. The findings revealed a framework of six components with 17 indicators. Current practices demonstrated high implementation levels (x̄ = 3.16–3.19) across all components, with decision-making and trust-building rating highest (x̄ = 3.19). Desired states achieved highest ratings (x̄ = 4.61–4.64) across all dimensions. Priority needs analysis (PNIModified) identified vision setting as the most critical development area (PNI = 0.459), followed by implementation (PNI = 0.456) and decision-making/trust-building (PNI = 0.455). Based on these findings, development guidelines encompassing six strategic approaches were formulated and validated by nine expert evaluators, receiving highest-level validation ratings (x̄ = 4.97–4.99). The guidelines particularly emphasized systematic stakeholder engagement and collaborative leadership practices, providing PAO schools with concrete strategies for enhancing participatory management effectiveness and academic outcomes.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".