Guidelines for Developing Graduate Programs in Educational Administration at Buddhist University
Bibliographic record
Abstract
This study aimed to develop guidelines for the improvement of the Master of Education and Doctor of Education Programs in Educational Administration (Revised Curriculum B.E. 2563) at Mahamakut Buddhist University, Srithammarat Campus. Using the CIPP evaluation model—Context, Input, Process, and Product—as a conceptual framework, the study employed a mixed-methods approach, collecting data through questionnaires and interviews with 170 stakeholders, including current students, alumni, faculty members, employers, and academic experts. The questionnaire data were analyzed using mean and standard deviation, while interview data were interpreted through content analysis. The results revealed a high level of appropriateness in all four dimensions of the curriculum. Key recommendations from stakeholders emphasized the need for integrating Buddhist principles—such as ethical leadership, mindfulness, and compassion—into educational administration theory and practice. The study concluded that effective curriculum development in Buddhist universities should harmonize academic rigor with spiritual and moral values, producing graduates who are not only professionally competent but also morally grounded and committed to serving society. These findings contribute to the development of culturally responsive and ethically focused higher education curricula in Thailand.
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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 | 0.033 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".