Essential Curriculum Content for Automotive Body Painting at Vocational High Schools: The Delphi Technique
Bibliographic record
Abstract
The extensive content of the curriculum at vocational high schools (SMK) that students must master results in graduates not fully mastering their knowledge. Therefore, this study needs to be conducted to analyze the content of the SMK curriculum so that it can be curated into essential materials based on criteria of urgency, continuity, relevance, and applicability in the curriculum of the automotive body painting course at SMK. This research employs a modified Delphi technique in two rounds, involving a panel of experts consisting of 21 practitioners in the field of automotive painting. The questionnaire was developed concerning the objectives of the automotive body painting curriculum, comprising 16 contents evaluated using a four-level Likert scale. Data collection was carried out using Google Forms. Data analysis was conducted using descriptive statistics with the aid of Excel and SPSS release 27. The study results indicate that there are eight highly essential contents out of the 17 in the automotive body painting curriculum. These eight contents are 1) Implementation of procedures for preparing materials and equipment for repairs; 2) Implementation of panel preparation procedures; 3) Application of putty method; 4) Application of sanding method; 5) Application of masking methods; 6) Implementation of metal panel painting procedures; 7) Implementation of plastic panel painting procedures; and 8) Evaluation and resolution of painting failures. Therefore, in implementing the curriculum, teachers can focus on the highly essential materials to enable students to learn more optimally.
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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 | 0.042 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".