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All Journal Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) Jurnal Pendidikan Teknik Mesin Undiksha
Gunadi, Diandra Keisha
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Automotive Engineering Education Engineering Industrial & Manufacturing Engineering Mechanical Engineering

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Evaluasi Program Teaching Factory di Sekolah Menengah Kejuruan menggunakan CIPP Framework: Evaluation of the Teaching Factory Program in Vocational High Schools Using the CIPP Framework Prihantoro, Constantinus Rudy; Nugraha, Hari Din; Gumelar, Agung; Pratama, David; Gunadi, Diandra Keisha; Ibrahim, Fakhrizal Hafish
Jurnal Pendidikan Teknik Mesin Undiksha Vol. 13 No. 2 (2025)
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jptm.v13i2.95013

Abstract

Teaching Factory is a learning program that optimizes the curriculum, resources, and human capital in Vocational High Schools (SMK) of Centers of Excellence. The objectives of this study are: (1) to identify the level of conformity in the implementation of the Teaching Factory program by analyzing the Context, Input, Process, and Product (CIPP) at five SMKs in DKI Jakarta; and (2) to formulate recommendations for improving and developing the Teaching Factory program to be more effective and sustainable. This research employed a quantitative method with a CIPP-based evaluation instrument, involving vice principals of curriculum and facilities, teachers, and students as research subjects. The findings reveal that: (1) Context: average score 3.61 out of 4, the curriculum is relatively aligned with industry needs, but the understanding of educational regulations remains low due to limited dissemination of government policies to teachers and school management; (2) Input: average score 3.55, facilities and infrastructure are inadequate due to limited budgets and lack of regular equipment calibration, which reduces the quality of marketable products and human resources, although teachers’ pedagogical competence (avg. 3.8) is already good; (3) Process: average score 3.59, learning methods are less innovative as teachers still rely on lectures and employ ineffective assessment instruments, resulting in low collaboration among students and suboptimal graduate employability; (4) Product: average score 3.26, the quality of marketable products falls below industry standards due to weak school quality control systems and the underutilization of project-based assessment. These findings can serve as a basis for strategies and curriculum development that better prepare students for the labor market and help reduce the unemployment rate among SMK graduates.
DEVELOPING AN EVALUATION INSTRUMENT BASED ON THE TECHNOLOGY ACCEPTANCE MODEL FOR TEACHERS' ARTIFICIAL INTELLIGENCE READINESS Nugraha, Hari Din; Gumelar, Agung; AK, Tri Bambang; Anggrainy, Rani; Nurahman, Muhamad Habib Alfin; Gunadi, Diandra Keisha
Journal of Mechanical Engineering Education (Jurnal Pendidikan Teknik Mesin) Vol 12, No 2 (2025): December 2025
Publisher : Universitas Pendidikan Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/jmee.v12i2.88890

Abstract

In Indonesia, only 48% of vocational teachers demonstrate limited readiness to integrate Artificial Intelligence into learning. Addressing this issue requires a reliable instrument to evaluate prospective teachers' preparedness. This study developed and validated an instrument based on the Technology Acceptance Model to assess the digital competencies of Mechanical Engineering pre-service teachers in Indonesian vocational education. The research quantitatively evaluated four constructs: Perceived Usefulness, Perceived Ease of Use, Behavioral Intention, and Self-Efficacy. Involving 100 respondents from a community service program, the development followed three stages: preparation, expert content validation using Aiken’s V, and construct validation via Exploratory Factor Analysis. Results confirmed unidimensional structures for all constructs. Perceived Usefulness and Perceived Ease of Use showed strong validity with high eigenvalues (4.984 and 5.063) and explained variance (49.8% and 50.6%). The Kaiser-Meyer-Olkin measure confirmed sampling adequacy (0.833–0.909). Aiken’s V analysis indicated high content (0.83), construct (0.81), and linguistic validity (0.85). Key findings highlight deficiencies in Artificial Intelligence tool proficiency, emphasizing the need for curriculum alignment with Industry 4.0 demands. This study provides a validated tool to guide teacher training and policy, effectively addressing the digital divide in vocational education and bridging a critical gap between theory and practice.
Co-Authors Agung Gumelar AK, Tri Bambang Anggrainy, Rani Ibrahim, Fakhrizal Hafish Nugraha, Hari Din Nurahman, Muhamad Habib Alfin Pratama, David Prihantoro, Constantinus Rudy