An Innovative Leadership Model for School Administrators to Enhance Instructional Quality in Secondary Educational Service Area Offices in Northeast Thailand
Bibliographic record
Abstract
This research aimed to develop an innovative leadership model for school administrators to enhance the quality of instructional management among teachers within Secondary Educational Service Area Offices in Northeast Thailand. The study employed a research and development (R&D) methodology conducted in two phases. Phase 1 investigated current conditions and components of innovative leadership for enhancing instructional management quality. Research instruments included a five-point Likert scale questionnaire and semi-structured interviews. The sample consisted of 478 school administrators and teachers selected through stratified random sampling, based on Krejcie and Morgan’s (1970) sample size determination table. Additionally, three school administrators recognized for exemplary practices were selected through purposive sampling for in-depth interviews. Data were analyzed using descriptive statistics (mean, percentage, and standard deviation), Exploratory Factor Analysis (EFA), and content analysis. Results showed that overall, innovative leadership was at a moderate level. EFA identified four components with initial eigenvalues greater than 1, explaining 79.67% of cumulative variance: (1) Creating an Innovative Vision, (2) Developing Leadership and Innovative Culture, (3) Creative Innovation Thinking, and (4) Utilizing Digital Technology for Innovative Instructional Management. Phase 2 involved developing the innovative leadership model to enhance instructional quality. Twelve experts validated the model through a peer review process. The model consisted of five key elements: principles, objectives, implementation methods (covering four dimensions: innovative vision, innovative organizational culture, creative innovation thinking, and digital technology utilization), model evaluation guidelines, and conditions for success. Overall suitability and feasibility of the model were rated at the highest level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 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".