Program Development for Enhancing Competencies of Vocational College Teachers in Mechatronics and Robotics under the Office of the Vocational Education Commission
Bibliographic record
Abstract
The purposes of this research were to 1) study the components and indicators of teacher competency in Mechatronics and Robotics, 2) study the current states, desirable states, and the needs for teacher competency development in the Mechatronics and Robotics Department, 3) design and development of teacher competency-enhancing programs in Mechatronics and Robotics, and 4) study the results of implementing the teacher competency-enhancing program in the Mechatronics and Robotics Department. This research was research and development conducted in 4 phases following the research purposes. The results showed that 1) Components and indicators of teacher competency in Mechatronics and Robotics have 5 components and 36 indicators confirmed by experts are appropriate at the highest level. 2) The current state of teacher competency in the Mechatronics and Robotics Department overall is moderate, and desirable condition overall is at the highest level, The competency development methods consist of (1) Self-study (2) Training (3) Workshops (4) Study visits and (5) Practice in the workplace, and the priorities of the needs to develop competencies are (1) Self-development (2) Ethics and professional ethics of teachers (3) Learning measurement and evaluation (4) Curriculum administration, and learning management (5) Building relationships and cooperation with communities for learning management, respectively. 3) Teacher Competency-enhancing Program in the field of Mechatronics and Robotics consists of (1) Principles, (2) Objectives, (3) Models and methods for development, (4) Content and development activities, amounting to 5 modules, and (5) Evaluation. The program evaluation results by qualified experts are appropriateness, utility, and possibility at the highest level. 4) The results of implementing the teacher competency-enhancing program in Mechatronics and Robotics were found as follows: (1) Pre-development knowledge of teachers in Mechatronics and Robotics education showed an average score of 17.30 out of 30 (57.66%), while post-development knowledge increased to an average score of 26.30 out of 30 (87.67%). (2) Overall, teachers’ competencies improved from a moderate level to the highest level after program implementation. (3) The satisfaction evaluation of program participants indicated the highest level of satisfaction in overall and each aspect.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".