Management Innovation for Thai Language and Culture Program of International Schools in Thailand Based on Concept of Agile Learner Characteristics
Bibliographic record
Abstract
The research aimed to develop an innovation for the Thai language and culture program management based on the concept of agile learner characteristics in Thailand. The study used the multiphase mixed-method approach which was conducted among 97 international schools in Thailand. The research findings revealed that management innovation for Thai language and culture program of international schools in Thailand based on the concept of agile learner characteristics which was titled “SWABK”. It consisted of three main components that promoting agile learner characteristics of seeking challenging situations, being a cognitive thinker, knowing what to do when facing uncertain situations, and welcoming feedback and criticism : (1) curriculum development: identifying learning outcomes that align with the need of Thai and global society and designing learning units by integrating Thai language and culture into global situations, (2) instruction: constructing learning tools and materials to be accessible anytime and anywhere, and (3) evaluation: utilizing the curriculum evaluation’s result to further develop learners to be ready for the future.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".