Parcipatory Action Research to Develop Local Wisdom-Integrated Environmental Education Lessons for Primary Schools in Mangrove Communities
Bibliographic record
Abstract
This study aimed to (a) investigate the problems and needs related to integrating local wisdom into classroom instruction, (b) explore the processes and outcomes of environmental education in teacher self-development for this integration, and (c) analyze key factors contributing to the success of teachers in applying local wisdom in their teaching. Local wisdom, deeply rooted in Thai cultural identity, offers valuable knowledge and techniques passed down through generations. It also reflects unique local contexts and can be used to enrich the learning experience in a way that aligns with students’ lives and the surrounding environment. Teachers play a crucial role in raising students’ awareness of local wisdom by embedding it in school curricula. However, current efforts to promote this integration face challenges due to varying teacher capacities, insufficient training, and context-specific needs. This study adopted a qualitative approach to identify a practical model and key success factors for supporting teachers’ professional growth. The findings revealed that successful integration depends on teacher motivation, collaborative learning, institutional support, and the relevance of professional development models to local contexts. This study provides insights into designing effective strategies to enhance the capacity of teachers, especially those in rural or marginalized areas, to connect curriculum with community knowledge, thereby promoting meaningful learning and cultural sustainability.
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".