The School-Based Professional Development for Teachers’ STEM Teaching: A Focus on Contextualized STEM Education in the Phuket Education Sandbox (Southern Regions of Thailand)
Bibliographic record
Abstract
This research aims to develop a professional development model for science teachers using a school-based approach to enhance STEM literacy. The model leverages outdoor STEM education resources in southern Thailand. The study focuses on primary-level science and project-based teachers in Phuket Province, selected through purposive sampling from schools participating in an educational sandbox project with support from school administrators. The research employed a mixed-methods approach to design a professional development program emphasizing outdoor STEM activities and incorporating local Phuket contexts. Data collection involved STEM literacy assessments, lesson plan evaluations, classroom observations, and focus group interviews. Quantitative data were analyzed statistically, while qualitative data underwent content analysis over a one-year period. The project began in 2023 with a needs assessment, tool development, and a draft model, followed by implementation in 2024. Findings highlight that school-based coaching and ongoing lesson design workshops, core elements of the program, improved teachers’ understanding of STEM integration. Teachers designed learning activities rooted in local contexts and engineering design principles, fostering interdisciplinary connections within a STEM framework. This approach enabled them to integrate STEM concepts into primary education while emphasizing continuous assessment of student outcomes and competencies. Despite the program’s success, challenges remain, particularly in allocating time for curriculum implementation and securing stronger administrative support. Nevertheless, the program advanced STEM literacy by using local contexts as a platform, aligning with community-based education goals and enhancing primary-level STEM education practices.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".