Program Development for Enhancing Teachers’ Training Competencies for Working in Mutual Enterprise Between Entrepreneurs and Educational Institutions under the Office of the Vocational Education Commission in the Eastern Economic Corridor (EEC)
Bibliographic record
Abstract
Abstract This research aims 1) to study the components and indicators of teachers’ training competencies for working in the mutual enterprise, 2) to study current states, desirable states, methods of development, and necessary needs of teachers’ training competencies for working in the mutual enterprise, 3) to design and develop the program for enhancing teachers’ training competencies for working in the mutual enterprise, and 4) to study the results of implementing the program for enhancing teachers’ training competencies for working in the mutual enterprise. This research was research and development that was conducted in 4 phases following the research objective. The results showed that 1) components and indicators of teachers’ training competencies consist of 5 components, 30 indicators, confirmed by 7 qualified experts is appropriate at the highest level. 2) The current state of teachers’ training competencies as a whole was at a moderate level, and the desirable state overall was at the highest level, Competency development methods include (1) self-development (2) training (3) workshops/learning exchanges (4) coaching (5) mentors and mentoring, and priorities of competency development needs were (1) preparation of a professional training plan, (2) measurement and evaluation of vocational, (3) coaching techniques, (4) coaching psychology, and (5) teacher trainers and the ethics of the teaching profession, respectively. 3) The program for enhancing teachers’ training competencies consists of (1) Principles, (2) Objectives, (3) Contents and methods of development consisting of 5 content modules, and 5 methods of development, (4) Measurement and Evaluation, (5) Expected outcomes, and (6) Success condition. The result of the program evaluation by 5 qualified experts is appropriate, useful, and possible at the highest level. 4) The results of implementing the program to enhance teachers’ training competencies in the workplace found that (1) the knowledge of the teachers training in the workplace before the development received an average score of 17.53 (58.44%) out of a full score of 30 after development received an average score of 25.86 (86.22%). Knowledge after development was higher than before development and was statistically significant at the .01 level. (2) The overall performance before development was moderate after development was at the highest level, and (3) the satisfaction assessment results of program participants overall and each aspect were at the highest level.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".