Teaching Competency Development Model of Instructor in Bachelor of Technology Degree in Automotive Technology Program for Institute of Vocational Education
Bibliographic record
Abstract
Providing higher education is crucial for the development of human resources. This research aims to develop a model for developing teaching competencies of teachers in the Bachelor of Technology Program in the Automotive Technology Department, Institute Vocational Education. Research and Development was divided into 3 phases: Phase 1 to study the conditions, problems, and approaches to developing teaching competence of teachers, using in-depth interviews with 10 teachers and 5 Experts. Phase 2 develop a model for developing teaching competence of teachers, using group discussions with 10 experts; and finally, Phase 3 to study the effectiveness of the Teaching Competency Development Model. A model for developing teaching was the development of the 4K4C+KCV Competency Development Model for teachers in the Automotive Technology program at vocational institutes. This model includes three basic components (KCV): Knowledge, Competencies, and Values, while the process of developing teaching competency comprises four steps (4K4C): 1. Know with understanding - Create knowledge by yourself, 2. Know in detail - Create cooperation, 3. Know with expertise - Create coordination, 4. Know to advise - Create work and leadership roles. The quality of the teaching competency development model it found high level. The overall quality of the teaching competency development model for teachers in the Bachelor of Technology Program in Automotive Technology at the Institute Vocational Education is at a high level.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".