An AI-Driven Framework for Learning Analytics and Operational Optimization in Technology and Vocational Education: Bridging Industrial Engineering and Informatics
DOI:
https://doi.org/10.21831/jpv.v15i3.95617Keywords:
Artificial Intelligence in Vocational Education;, Learning Analytics, Operational Optimization, Design Science Research, Teaching Factory SystemsAbstract
This study introduces an artificial intelligence–based framework that brings together learning analytics and operational optimization to respond to ongoing issues in technology and vocational education. Although vocational institutions have increasingly incorporated digital technologies into their learning environments, the use of data to support both educational outcomes and operational decision-making is still largely disconnected. In many cases, learning-related data are examined separately from production-oriented activities such as scheduling, resource allocation, and process efficiency, even though these activities are integral to teaching factory and laboratory-based learning. This research addresses this disconnect by integrating perspectives from industrial engineering and informatics within a single AI-oriented framework. The framework is developed to accommodate and analyze diverse data originating from learning processes, practical activities, and operational workflows. Machine learning methods are used to represent learner performance, competency progression, and process patterns, while optimization techniques support decisions related to task distribution, scheduling, and the use of resources. Instead of being limited to a specific application or setting, the framework is designed with adaptability in mind, enabling its use across various vocational and engineering education contexts. An empirical study was conducted in a technology-focused vocational education environment to examine the feasibility and effectiveness of the proposed framework. The findings indicate that combining learning analytics with operational optimization yields insights that are more consistent and meaningful than approaches that treat these aspects independently. The AI-based framework improves the reliability of competency assessment and performance prediction, while also contributing to more efficient management of production-like learning activities. These results highlight the potential of AI to support vocational education in a more comprehensive manner. This study contributes to existing research by presenting an interdisciplinary framework that moves beyond isolated technological tools and offers a more integrated perspective on AI adoption in vocational education. In addition to providing practical insights for educators and institutional managers, the framework serves as a conceptual reference for future studies that seek to connect artificial intelligence, industrial engineering, and technology-oriented education.
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