Skills and Knowledge Competency of Technical and Vocational Education and Training Graduate
Bibliographic record
Abstract
The Education Development Plan of Malaysia (Higher Education) carry the nation’s aspiration to empower the technical and vocational education and training (TVET) in Malaysia. The emphasis on the development of high quality TVET graduates demands teachers and instructors of TVET who are highly knowledgeable and skilled. Thus, the emphasis on the quality of TVET teachers’ education training of Faculty of Technical and Vocational Education (FTVE), Sultan Idris Education University (UPSI) has become an interesting issue that needs exploration. To evaluate the effectiveness of the education graduates, a quantitative survey research design using the Stuffelbeam evaluation model was carried out. The samples were FTVE graduates that have been placed in secondary schools and vocational colleges all over Malaysia. A total of 111 respondents have answered the questionnaire. The research findings showed that the level of professional knowledge, skills and practice were high. However, parallel to the concept of continuous improvement, the elements that are at the level of moderate will be evaluated for improvement. These research findings were expected to give some information to policy maker in TVET Teachers Training Provider to increase the quality of TVET graduates in UPSI specifically and Malaysia in general in order to uphold the aspiration to become a developed nation by 2020.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".