Guidelines for the Application of Design Thinking Processes in School Administration of School Administrators Under the Secondary Educational Service Area Office, Phitsanulok, Uttaradit
Bibliographic record
Abstract
The purpose of this research is twofold: 1) to study the application of the design thinking process in school administration under the Secondary Educational Service Area Office, Phitsanulok, Uttaradit, and 2) to explore the implementation of design thinking process in school administration of school administrators under the Secondary Educational Service Area Office, Phitsanulok, Uttaradit. This is a qualitative research study conducted using a sample of 57 educational administrators and 7 qualified personnel as participants. The research tools utilized include document analysis, questionnaires, and interviews, with statistical analysis involving mean values and standard deviations. The research findings indicate that: 1) The application of the design thinking process in school administration of school administrators is at its highest level. When considering each aspect, it was found that the model prototypes had the highest average, while data synthesis had the lowest average. 2) In terms of guidelines for applying design thinking processes in school administration, it was observed that confidence, empathy in work, and awareness of problems with open-ended questioning encourage creativity. Motivating and encouraging the expression of different ideas by participating in the development of models for use in problem-solving and the collection of information of suggestions, for improvement and development were key factors.
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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 | 0.108 | 0.162 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".