Developing the potential of work-based learning: New challenges in Thailand's TVET system and the public-private partnership at Rajamangala University of Technology Lanna
Bibliographic record
Abstract
This study examines the role of work-based learning (WBL) in meeting the evolving demands of Industry 4.0 and digital transformation within Thailand's Technical and Vocational Education and Training (TVET) system. It assesses the current state of WBL implementation in Thai TVET and proposes models to enhance its effectiveness. Using a qualitative approach, the research focuses on two public-private partnership models at Rajamangala University of Technology Lanna (RMUTL): the school-in-factory (SiF) model and the Tripartite Education System. Insights were gathered through semi-structured interviews with five TVET experts, identifying key areas for WBL improvement. Despite increased recognition of WBL in Thailand, challenges remain, including limited industry engagement, inadequate infrastructure, and insufficient teacher training. The study emphasizes the need for stronger public-private partnerships, greater industry participation, and ongoing enhancement of WBL programs. The findings provide valuable insights for policymakers, educators, researchers, and industry leaders in Thailand and the ASEAN region. By advancing effective WBL models, such as the SiF model and the Tripartite Education System, Thailand can cultivate a skilled workforce essential for economic growth and innovation. Furthermore, the study highlights the importance of ongoing evaluation and improvement to ensure that WBL programs remain relevant and effective in the dynamic context of Industry 4.0.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".