Management Strategies for Higher Education Institutions Based on the Principles of Education for Sustainable Development in the Lower Northern Region
Bibliographic record
Abstract
Education for sustainable development is globally acknowledged as crucial for fostering knowledge, skills, and values essential for sustainable living. This research aimed to (1) examine the current and desired conditions of administrative strategies in higher education institutions in the lower northern region based on the concept of education for sustainable development, and (2) develop corresponding strategies focusing on economic, social, environmental, and systemic dimensions. A mixed-method approach was employed, with a sample of 243 administrators and personnel from 12 universities in the region. Research instruments included a conceptual framework assessment, a questionnaire, and structured interviews. The findings revealed that current administrative practices ranked as follows: (1) curriculum development, (2) teaching and learning management, and (3) educational measurement and evaluation. While all aspects showed a good level of development, the desired conditions indicated the need for further improvement, with the ranking of (1) curriculum development, (2) educational measurement and evaluation, and (3) educational management. The study identified three core strategies: (1) reforming integrated curriculum design to align with sustainable development by emphasizing comprehensive systems that connect theory to practice; (2) enhancing the quality of educational measurement and evaluation to support sustainability-focused skills and concepts; and (3) transforming teaching and learning management by utilizing technology to foster an environment conducive to learning and innovation. These strategies are essential for guiding universities toward sustainable development and align with Thailand’s higher education standards focused on producing skilled, knowledgeable, and sustainability-minded graduates.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".