Management of Teaching Factory (TEFA) for Achieving Vocational School Graduate Competencies
Bibliographic record
Abstract
This study aims to analyse the implementation of Teaching Factory (TEFA) management to achieve graduate competencies at vocational schools. Teaching Factory (TEFA) is a production and service-based learning model that integrates school learning with the needs of the industrial world. This approach aims to produce graduates who are competent, work-ready, and possess skills according to industry standards. The results of this study indicate that the implementation of TEFA management at schools, encompassing planning, organising, implementation, evaluation, and follow-up, has been carried out effectively. The research used a descriptive method with a qualitative approach, involving observation, interviews, and documentation. The findings reveal that TEFA management implementation in schools includes planning based on industry needs, integrating learning with real-world projects, and evaluating outcomes based on work performance. Supporting factors for TEFA's success include close cooperation between schools and industries, the availability of supporting facilities, competent teachers, and effective follow-up management. However, this study also identified several obstacles, including limited facilities, a lack of continuous teacher training, and challenges in meeting the evolving needs of industry. Through optimal management, the Teaching Factory is expected to enhance the competitiveness of graduates and address labour market needs.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 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".