Effectiveness of 6C Skills Activities on Industrial Design Course (RBI)
Bibliographic record
Abstract
The purpose of this study is to assess how well the 6C Skills activities are implemented in the Industrial Design (RBI) course at the Faculty of Technical and Vocational Education (FPTV), Universiti Tun Hussein Onn Malaysia (UTHM), regarding the soft skills mastery and project results of the students. Participants included 92 Bachelor of Vocational Education (SMPV) students, 50 of whom were in the treatment group (TG) and 42 of whom were in the control group (CG). 49 TG lecturers and 44 CG lecturers made up the total of 93 lecturers that participated. Project assessments and questionnaires were utilised to gather data for the study, which had a quasi-experimental design. Since the normality test revealed an anomalous distribution of the data, non-parametric statistical tests such the Mann-Whitney U and Wilcoxon Signed-Rank were used to analyse the data. The findings indicated that TG and CG significantly differed in their attainment of student project outcomes (U = 566.500, p <.001), with TG performing better. A significant increase in TG for all subcomponents was also seen in the post score of 6C Skills competence (Z varied from -5.533 to -5.996, p<.001), but the increase in CG was less pronounced. These results demonstrate how well the 6C Skills-based teaching strategy works to enhance 21st-century abilities. This research has significant ramifications for the advancement of TVET pedagogy that is more dynamic, comprehensive, and pertinent to the demands of the future 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".