Strategies to Manage Vocational Education to Excellence
Bibliographic record
Abstract
This research aimed to develop strategies to manage vocational education to excellence using the Research and Development (R&D) methodology. The study included two phases. Phase 1 involved analyzing fundamental data and needs for vocational education management. The sample was 206 school directors from eight types of vocational institutions under the Office of the Vocational Education Commission. The researchers determined the sample using Taro Yamane’s formula (stratified random sampling) and interviews with experts from three selected model vocational institutions. Phase 2 involved creating strategies through discussions with experts from nine higher education and vocational institutions. Data analysis included percentages, means, standard deviations, and the modified Priority Needs Index (PNImodified). Results showed that the current status of basic data was at a high level overall, while the expected status was at the highest level, with a PNImodified of 0.179. The strategies for vocational education management towards excellence included: 1) Enhancing professional management efficiency; 2) Developing competency-based curriculum quality to international standards; 3) Improving teaching and learning quality; 4) Developing teacher and staff quality to specialized expertise; 5) Building comprehensive human resource development networks; and 6) Enhancing graduate quality to high-competency human resources. The evaluation of these strategies indicated that they were highly appropriate and feasible.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| 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".