cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Novianita Rulandari
Contact Email
journal@idscipub.com
Phone
+62885218596868
Journal Mail Official
journal@idscipub.com
Editorial Address
Gondangdia Lama Building 25, RP. Soeroso Street No.25, Jakarta, Indonesia, 10330
Location
Kota adm. jakarta pusat,
Dki jakarta
INDONESIA
Eduscape: Journal of Education Insight
Published by PT Penerbit Ilmiah Indonesia
ISSN : -     EISSN : 30265231     DOI : https://doi.org/10.61978/eduscape
Core Subject : Education,
Education
Eduscape: Journal of Education Insight is a journal published by Indonesian Scientific Publication, published original scholarly papers across the whole spectrum of educations. The journal attempts to assist in the understanding of the present and potential ability of education to aid in the recording and interpretation of international education practices.
Arjuna Subject : Ilmu Sosial - Pendidikan
Articles 51 Documents
 2   3   4   5   6 
Implementation of Deep Learning in Teaching Factory as a Strategy for Enhancing Industrial Competencies: A Systematic Literature Review Herianto; Gunawan, Aditia Gustiana; Kamdi, Waras
Eduscape : Journal of Education Insight Vol. 4 No. 1 (2026): January 2026
Publisher : Indonesian Scientific Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61978/eduscape.v4i1.1244

Abstract

The advancement of Industry 4.0 has significantly increased the demand for industrial competencies aligned with intelligent, data-driven manufacturing systems. Deep learning, as a core artificial intelligence technology, plays a critical role in smart factories, particularly in computer vision, predictive analytics, and automated decision-making. In parallel, the Teaching Factory model has emerged as a strategic educational approach to bridge the gap between vocational education and real industrial practices. This study conducts a Systematic Literature Review (SLR) on the integration of deep learning and pedagogical approaches within Teaching Factory and automated manufacturing learning environments, with a focus on industrial competency development. Using a structured and transparent review protocol, peer-reviewed journal articles were analyzed to identify instructional practices, learning theories, targeted competencies, and research methodologies. The review indicates that while Teaching Factory models emphasize production-based learning, deep learning has not yet been systematically embedded into their pedagogical frameworks. Existing studies predominantly address technical and cognitive competencies, with limited attention to transversal and employability competencies. Methodologically, the literature is largely dominated by conceptual frameworks and short-term case studies, underscoring the need for more empirical and longitudinal research. This review contributes by synthesizing current evidence, clarifying research gaps, and proposing directions for pedagogically grounded and industry-aligned Teaching Factory models that integrate deep learning to support comprehensive industrial competency development

Page 6 of 6 | Total Record : 51

 2   3   4   5   6 

Filter by Year

2023 2026


Reset
Filter By Issues
All Issue Vol. 4 No. 1 (2026): January 2026 Vol. 3 No. 4 (2025): October 2025 Vol. 3 No. 3 (2025): July 2025 Vol. 3 No. 2 (2025): April 2025 Vol. 3 No. 1 (2025): January 2025 Vol. 2 No. 4 (2024): October 2024 Vol. 2 No. 3 (2024): July 2024 Vol. 2 No. 2 (2024): April 2024 Vol. 2 No. 1 (2024): January 2024 Vol. 1 No. 1 (2023): October 2023