Vol. 1: The Excellence of Technical Vocational Education and Training (TVET) Institutions in Korea: Yeungjin College Case Study
Bibliographic record
Abstract
To tackle the issue of skill shortages, many governments are restructuring their respective school systems into more demand-driven systems, which are expected to improve overall school outcomes and external efficiency. In order to assist TVET institutes and governments with the development of innovative methods to improve the outcomes, this study seeks to provide suggestions drawn from an in-depth case study of a successful TVET school. The selection criteria for the case study’s subject required a school to have high external outcomes, i.e. graduate employment rate. The study then assessed whether or not the select school possesses four premise factors (high quality teacher, relevant curricula, strong leadership, and school-industry linkages) and how these factors contribute to the improvement of the graduate employment rate. The study gathered data via survey and interviews of both faculty and students. As for the survey, 693 out of 1,400 juniors and 23 out of 71 professors responded. The interviews were a face-to-face, one-on-one style with structured, open-ended questions. Ten students and ten professors were interviewed separately in a closed room, and 60 minutes was allotted for each session. After coding the raw data, certain themes emerged. The findings suggest that Yeungjin College possesses all the stated premise factors, and the factors directly and/or indirectly influences the graduate employment rate via the enhancement of employability. Additionally, the most determining factor can be altered within different contexts (e.g. TVET policy, labor market) and times.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".