A Sustainable Development Model for Promoting a Happy Workplace in Autonomous Universities in Thailand
Bibliographic record
Abstract
The work environment significantly impacts employee performance, job satisfaction, and organizational growth. Poor working conditions can lead to stress and negatively affect physical and mental health. This study aims to explore approaches and theories for fostering a happy organization by applying Kolb’s experiential learning theory, which includes four stages: (1) Concrete Experience – learning by doing, (2) Reflective Observation – observing and reflecting on experiences, (3) Abstract Conceptualization – forming ideas from gathered information, and (4) Active Experimentation – testing and refining concepts. These stages were adapted to the context of Thai public universities, resulting in the DICE-K model with five steps: (1) Direct Experience – learning through hands-on activities, (2) Integrated Reflection – linking current actions with prior knowledge, (3) Conceptualization – generating new ideas, (4) Execution – applying concepts in practice, and (5) Knowledge Management – sharing and sustaining knowledge. The model emphasizes collaborative learning, creativity, and sustainable knowledge management. Key activities, such as the Share & Show “KM Day” platform, inspire and promote good practices. The findings indicate that the DICE-K model is effective for fostering a happy organization in universities and holds the potential for adaptation in other institutions in the future.
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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 | 0.002 | 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".