Management Model for Internal Quality Assurance of Schools under Surat Thani Primary Educational Service Area Office
Bibliographic record
Abstract
This research aimed to: (1) investigate the management and challenges of internal quality assurance (IQA) in basic education schools in Surat Thani Province, and (2) develop a school-size-specific IQA management model. The study was conducted in two phases. Phase 1 involved in-depth interviews with 115 IQA teachers from high-performing schools in the NYY school group. The data were analyzed using ATLAS.ti 8 and categorized by school size. Phase 2 consisted of three steps: model drafting, expert validation through purposive focus groups, and evaluation of the model’s usefulness, feasibility, appropriateness, and accuracy using a five-point Likert scale questionnaire. The instrument had content validity indices ranging from 0.60 to 1.00 and reliability coefficients of 0.905 for small-sized schools and 0.880 for medium-sized schools. The findings revealed that small schools faced limitations in physical aspects, including inadequate facilities and outdated learning resources, insufficient personnel with multiple responsibilities, and external support not aligned with school contexts. Medium schools demonstrated better readiness in facilities and personnel but still faced constraints in specialized teachers, budget limitations for modern learning resources, and inadequate external support. And The developed internal quality assurance management models were categorized by aspects: for small schools, EDCR Model (Physical), PASDE Model (Personnel), and RPNCR Model (External Support); for medium schools, DDAE Model (Physical), MACCRC Model (Personnel), and PNTLCM Model (External Support). The overall evaluation of the models in terms of utility, feasibility, appropriateness, and accuracy was rated at the highest level. The study suggests that these models provide practical and effective strategies for NYY schools to overcome limitations and enhance their quality to achieve YYY status.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".