The Learning Leadership of School Administrators to Enhance the Quality of Education in Ubon Ratchathani Primary Educational Service Area Office 3
Bibliographic record
Abstract
This research examined learning leadership among school administrators for educational quality development in Ubon Ratchathani Primary Educational Service Area Office 3. The study aimed to: 1) assess learning leadership levels, 2) compare levels by position, work experience, and school size, and 3) identify development approaches. The sample included 323 administrators and teachers, with 6 participants selected for qualitative interviews through purposive sampling. A 5-point Likert scale questionnaire (reliability = .97) and structured interviews were used. Data analysis employed descriptive statistics, t-tests, F-tests, and Scheffe’s post-hoc tests. Findings indicated the learning leadership was at a high level overall. Significant differences (p < .01) were found across position, experience, and school size. Five development approaches were identified: 1) Team Learning—establishing shared objectives and promoting collaborative communication through technology; 2) Technology Utilization—developing technological skills and integrating technology in administration and learning management; 3) Creativity—fostering creative environments and supporting innovative teaching methods; 4) Learning-Conducive Environment—implementing participatory management, allocating resources, and developing safe learning spaces; and 5) Learning Innovation Development—promoting innovation application in learning processes and educational administration to enhance quality.
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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 | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".