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Record W4416037925 · doi:10.5430/jct.v14n4p274

Effectiveness of 6C Skills Activities on Industrial Design Course (RBI)

2025· article· W4416037925 on OpenAlexvenueno aff
Zuan Azhary Mohd Salleh, Syahril Syahril, Rahmat Azis Nabawi, Rizky Ema Wulansari, Dian Safitri, Mohamed Nor Azhari Azman, Nurulwahida Azid

Bibliographic record

VenueJournal of Curriculum and Teaching · 2025
Typearticle
Language
FieldSocial Sciences
TopicHigher Education and Employability
Canadian institutionsnot available
FundersUniversitas Negeri PadangUniversiti Tun Hussein Onn Malaysia
KeywordsVocational educationCompetence (human resources)BachelorWilcoxon signed-rank testNormalityTest (biology)Soft skillsStatistical analysisRetraining

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.722

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.356
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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