Industry-Oriented Computer-Aided Manufacturing Learning Models in Vocational Higher Education: A Multi-Case Qualitative Study

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Authors: Tri Adi Prasetya, Chrisna Tri Harjanto, Dwi Rahdiyanta
Cited by: 0

Abstract

Vocational higher education is required to produce graduates with Computer-Aided Manufacturing (CAM) competencies that align with rapidly evolving industrial demands. However, variations in learning models across institutions often result in inconsistent competency outcomes. This study aims to explore and analyze CAM learning models implemented in Diploma-level vocational higher education and examine their relevance to workplace requirements. Employing a qualitative multi-case study approach, data were collected from four vocational higher education institutions in Indonesia through observations, document analysis, and semi-structured interviews with lecturers and industry partners. The findings identify three dominant CAM learning models: textbook-based, industry-based, and internet literacy-based learning. The industry-based model demonstrates the most substantial alignment with workplace competencies, particularly in problem-solving and adaptability, while the textbook-based model remains effective for foundational conceptual understanding. Internet literacy-based learning supports independent learning but requires structured guidance to ensure depth of competency. The study highlights the importance of integrating soft skills, including leadership and problem-solving, into CAM learning models to enhance graduates’ employability in Industry 4.0. These findings provide practical insights for curriculum designers and vocational institutions in developing more industry-responsive CAM learning strategies

Fields of Study & Subjects

Higher Education and Employability Educational Leadership and Innovation Vocational and Entrepreneurial Education

Publication details

Journal / source
Jurnal Ilmiah Pendidikan Teknik dan Kejuruan
Publisher
Universitas Sebelas Maret
Volume
19
Issue
1
Pages
14–14
Publication date
5 January 2026
Language
ID