Driving SMT Competency-Based Curriculum with Area Potential Integration in Thailand’s Education Sandbox
Bibliographic record
Abstract
This study examines the integration of a Science, Mathematics, and Technology (SMT) Competency-Based Curriculum (CBC) within Thailand’s Education Sandbox initiative, established under the 2019 Education Sandbox Act, to foster innovation, reduce inequalities, and decentralize education management. The research aims to adapt and expand the SMT CBC to align with local contexts, enhancing students’ 21st-century skills such as critical thinking, problem-solving, and digital literacy. A three-phase methodology was employed: monitoring curriculum implementation in prototype schools, expanding it to network schools, and developing a systemic framework for broader application. Key findings emphasize the roles of school leaders, teachers, and stakeholders in successful implementation. Visionary leadership, teacher training in competency-based methods, and community resource contributions were identified as essential. Collaborative workshops and knowledge-sharing activities across four provinces facilitated best practice dissemination and sustainable educational networks. Despite challenges like limited teacher readiness and resource constraints, the SMT CBC shows promise in improving educational quality and equity. It prepares students for real-world applications while fostering collaboration among schools and communities. The study highlights the importance of localized policies, stakeholder collaboration, and continuous evaluation, contributing valuable insights for advancing competency-based education in Thailand and globally.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".