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An AI-driven framework for learning analytics and operational optimization in technology and vocational education: Bridging industrial engineering and informatics

Jurnal Pendidikan Vokasi · Vol. 15 No. 3 (2025) · hlm. 348–364

DOI
10.21831/jpv.v15i3.95617
ISSN
2476-9401, 2088-2866
Terbit
2025-11-24
Penerbit
ADGVI & Graduate School of Universitas Negeri Yogyakarta
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This study proposes an artificial intelligence (AI)-driven framework that integrates learning analytics with operational optimization to address the fragmentation between educational data and operational decision-making in technology and vocational education. Although vocational institutions increasingly adopt digital technologies, learning-related data are often analyzed separately from operational processes such as scheduling, task allocation, resource utilization, and process efficiency, despite their interdependence in teaching factory and practical learning environments. Drawing on perspectives from industrial engineering and informatics, the proposed framework integrates machine learning for analyzing learner performance and competency development with optimization techniques for supporting operational decision-making. The framework was designed to be adaptable across different vocational education contexts and was empirically evaluated in a technology-oriented vocational setting to assess its feasibility. The evaluation demonstrates the feasibility of integrating learner-related and operational information within a unified decision-support environment, enabling competency and performance information to inform decisions concerning practical tasks, scheduling, and resource allocation. Rather than positioning AI solely as a predictive or instructional tool, the framework conceptualizes it as a mechanism for connecting pedagogical and operational dimensions of vocational learning. This study contributes an interdisciplinary and system-oriented perspective on AI adoption in vocational education, providing a foundation for more integrated, context-sensitive, and data-informed decision-making in teaching factory and practical learning environments.

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11 penulis · 34 tautan kepenulisan bersama · 16 lintas organisasi

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  • Universitas Islam Negeri Maulana Malik Ibrahim
  • Yogyakarta State University
  • Indonesia Open University
  • Universitas Negeri Surabaya
  • International Islamic University Malaysia
  • Institut Teknologi Sepuluh Nopember
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Rekan penulisOrganisasiArtikel bersama
Fresy NugrohoUniversitas Islam Negeri Maulana Malik Ibrahim1
Dwi PebriantiInternational Islamic University Malaysia1
Moch FachriInstitut Teknologi Sepuluh Nopember1
I. Gusti Putu Asto BuditjahjantoUniversitas Negeri Surabaya1
Tri Mukti LestariUniversitas Islam Negeri Maulana Malik Ibrahim1
Dian MaharaniUniversitas Islam Negeri Maulana Malik Ibrahim1
Alfina NurrahmanUniversitas Islam Negeri Maulana Malik Ibrahim1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://journal.uny.ac.id/index.php/jpv/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">An AI-driven framework for learning analytics and operational optimization in technology and vocational education: Bridging industrial engineering and informatics</dc:title>
	<dc:creator>Nurdiyanto, Heri</dc:creator>
	<dc:creator>Hernandes , Leonel</dc:creator>
	<dc:creator>Hammad, Jehad A. H.</dc:creator>
	<dc:creator>Kindiasari, Aktansi</dc:creator>
	<dc:subject xml:lang="en-US">artificial intelligence</dc:subject>
	<dc:subject xml:lang="en-US">design science research</dc:subject>
	<dc:subject xml:lang="en-US">learning analytics</dc:subject>
	<dc:subject xml:lang="en-US">operational optimization</dc:subject>
	<dc:subject xml:lang="en-US">teaching factory</dc:subject>
	<dc:subject xml:lang="en-US">vocational education</dc:subject>
	<dc:description xml:lang="en-US">This study proposes an artificial intelligence (AI)-driven framework that integrates learning analytics with operational optimization to address the fragmentation between educational data and operational decision-making in technology and vocational education. Although vocational institutions increasingly adopt digital technologies, learning-related data are often analyzed separately from operational processes such as scheduling, task allocation, resource utilization, and process efficiency, despite their interdependence in teaching factory and practical learning environments. Drawing on perspectives from industrial engineering and informatics, the proposed framework integrates machine learning for analyzing learner performance and competency development with optimization techniques for supporting operational decision-making. The framework was designed to be adaptable across different vocational education contexts and was empirically evaluated in a technology-oriented vocational setting to assess its feasibility. The evaluation demonstrates the feasibility of integrating learner-related and operational information within a unified decision-support environment, enabling competency and performance information to inform decisions concerning practical tasks, scheduling, and resource allocation. Rather than positioning AI solely as a predictive or instructional tool, the framework conceptualizes it as a mechanism for connecting pedagogical and operational dimensions of vocational learning. This study contributes an interdisciplinary and system-oriented perspective on AI adoption in vocational education, providing a foundation for more integrated, context-sensitive, and data-informed decision-making in teaching factory and practical learning environments.</dc:description>
	<dc:publisher xml:lang="en-US">ADGVI &amp; Graduate School of Universitas Negeri Yogyakarta</dc:publisher>
	<dc:date>2025-11-24</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.uny.ac.id/index.php/jpv/article/view/95617</dc:identifier>
	<dc:identifier>10.21831/jpv.v15i3.95617</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Pendidikan Vokasi; Vol. 15 No. 3 (2025); 348-364</dc:source>
	<dc:source>2476-9401</dc:source>
	<dc:source>2088-2866</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.uny.ac.id/index.php/jpv/article/view/95617/26015</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Heri Nurdiyanto, Leonel Hernandes , Jehad A.H Hammad, Aktansi Kindiasari</dc:rights>
	<dc:rights xml:lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>