Model Development for Effective Vocational Education Management of Bachelor Degree Program in Institute of Vocational Education in the Northeast
Bibliographic record
Abstract
The present research aimed to 1) study the components and indicators of effective management for bachelor's degree programs, 2) assess the current condition, desired condition, and necessary development requirements, 3) create and develop the model and a user manual, and 4) evaluate the appropriateness, feasibility, and benefits of the model and the manual. The research employed a mixed-methods approach and conducted four phases according to objectives. The research results revealed that 1) the components and indicators of effective management for bachelor's degree programs consist of seven components and 41 indicators, and the results of the assessment of suitability by 5 qualified experts were at the highest level. 2)The overall current conditions are moderate, the desired condition, overall, and each component, are at the highest level, and the necessary needs, in order of importance by component, are as follows: 1) Personnel Management, 2) Vocational Training, 3) Student Development, 4) Teaching and Learning Management, 5) Curriculum Management, 6) Cooperation with Enterprises, and 7) Measurement and Evaluation. 3) The model consists of (1) principles of the model, (2) objectives of the model, (3) operational systems and mechanisms of the model, (4) components/indicators and practices driven by the PDCA quality cycle, (5) conditions for success, and (6) effectiveness evaluation. The manual consists of Chapter 1: Introduction, Chapter 2: Effective Management Model, Chapter 3: Implementing the Effective Management Model, and Chapter 4: Evaluation of the Effective Management Model. 4) Nine experts evaluated the model and the manual for using it and found that they were the most appropriate, feasible, and beneficial.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".