Chen Yu
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Enhancing Student Engagement with AI-Driven Personalized Learning Systems Zaharuddin; Chen Yu; Yao, Goh
International Transactions on Education Technology (ITEE) Vol. 3 No. 1 (2024): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i1.662

Abstract

This paper explores the impact of AI-driven personalized learning systems on enhancing student engagement in educational settings. With the increasing integration of artificial intelligence (AI) in various sectors, education is also experiencing a shift towards more adaptive and personalized learning environments. The study investigates how personalized learning paths, powered by AI algorithms, can address diverse learning needs and promote greater involvement from students. Through a comprehensive analysis of engagement metrics, pre-and post-implementation comparisons, and surveys from both students and educators, this research identifies key factors that contribute to improved student motivation, interaction, and academic performance. The findings suggest that AI-driven systems not only provide tailored learning experiences but also foster a deeper connection between students and their learning content. The paper concludes with recommendations for future research and practical applications in educational institutions to further optimize the use of AI for enhancing student engagement.
Utilizing Wearable Technologies to Foster Outcome-Based Education in Learning Factories Sofiyan; Lucas Lawrence; Lily Maria Evans; Khaizure Mirdad; Chen Yu
International Transactions on Education Technology (ITEE) Vol. 3 No. 2 (2025): International Transactions on Education Technology (ITEE)
Publisher : Pandawan Sejahtera Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/itee.v3i2.793

Abstract

The integration of wearable technologies into educational settings has opened new avenues for enhancing experiential and outcome-based learning, particularly in practice-oriented environments such as learning factories. This study investigates how wearable devices such as smart glasses, biometric trackers, and haptic feedback systems can be effectively utilized to support real-time performance monitoring, contextual learning, and continuous skill assessment in engineering and manufacturing training. The objective of this research is to explore the potential of these technologies in reinforcing the principles of outcome-based education (OBE), where learner competence is measured through demonstrable performance rather than passive knowledge acquisition. A mixed-method approach was adopted, combining qualitative field observations and interviews with quantitative data collected through controlled experiments involving wearable technology use in a simulated learning factory environment. The findings reveal that wearables significantly contribute to increased learner engagement, improved task efficiency, and enhanced feedback mechanisms, leading to better alignment between learning outcomes and industrial competency demands. Moreover, the results suggest that wearable-assisted learning environments foster reflective learning and support personalized instruction by capturing granular data on learner behaviors and outcomes. This research concludes that integrating wearable technologies into learning factories not only enhances the quality and relevance of vocational and technical education but also supports broader sustainable development goals by promoting inclusive, adaptive, and technologically enriched learning systems. The study provides a foundation for future research into scalable, data-driven educational models and the role of emerging technologies in transforming skill-based education.