Evaluate the Vocational School Graduate's Work-readiness in Indonesia from the Perspectives of Soft skills, Roles of Teacher, and Roles of Employer
Bibliographic record
Abstract
Over the past few decades, there has been a widening disparity in the abilities required for various skills, and employees are expected to possess the necessary requirements in this age of technology and industrial advancement. Education institutions around the globe face a formidable task in meeting this demand for skilled workers. Therefore, this research discusses the perspective of vocational education students on job readiness. A quantitative survey of nine soft skill indicators of vocational education students was conducted. Multiple linear regression analysis was conducted on an online survey of 530 students from 29 vocational schools across Indonesia. The data focuses on the soft skills currently attracting industry interest. Furthermore, soft skills are closely related to student work readiness, while vocational education learning only focuses on developing hard skills. Based on the needs of the industry, soft skills are progressively becoming essential qualifications. Therefore, vocational education needs to include soft skills in learning objectives, and the research reported five strong and three weak indicators. Soft skills can be increased through integrated and effective learning. This study's findings are that six indicators of soft skills have been developed enormously, and three other hands are weak. The presence of employers in the learning process is beneficial in developing soft skills for work readiness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".