Evaluation of Product-Based Education Training Class at Vocational High School using the CIPP Model
Bibliographic record
Abstract
An employer is commonly dissatisfied with the skills of Vocational High School (VHS) graduates and suggest schools carry out learning innovations. This dissatisfaction led to the response of Warga VHS, through the implementation of a product-based education training class. Therefore, this study aims to evaluate industrial-class best practices regarding the implementation of Product-Based Education Training (PBET) in VHS, using the Context, Input, Process, and Product (CIPP) model. This evaluative method was used and conducted at the Warga VHS Surakarta, which organizes PBET industrial class. Using a purposive sampling technique, the study samples were selected, containing 41 students, 8 teachers, 2 alums, 2 parents, and 1 industrial manager. Data collection was also obtained through questionnaires, interviews, and documentation. In this process, the validity of the questionnaire items used the moment product correlation. Based on the results, students' context, input, process, and product evaluation had average scores of 4.48, 4.25, 4.39, and 4.25, respectively. Meanwhile, the teachers' average values were 4.29, 4.36, 4.23, and 4.5 for the context, input, process, and product evaluation, respectively. In this case, the entirety of these values was included in the very high category. This indicated that the implementation of PBET improved graduate skills and sustainably strengthened cooperation with the industry.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".