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All Journal International Transactions on Education Technology (ITEE)
Al-Kamari, Omar Arif
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Control & Systems Engineering Social Sciences

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Leveraging Data Utilization and Predictive Analytics: Driving Innovation and Enhancing Decision Making through Ethical Governance Br. Karo, Mestiana; Miller, Bella Pertiwi; Al-Kamari, Omar Arif
International Transactions on Education Technology (ITEE) Vol. 2 No. 2 (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.v2i2.593

Abstract

Advances in information technology have fueled an exponential increase in the volume and diversity of data generated by organizations and individuals. In this era, Data Science has emerged as a crucial discipline for uncovering hidden patterns within data, thereby facilitating smarter decision-making processes. This paper presents a comprehensive and up-to-date overview of the challenges and opportunities in the application of Data Science, with a particular focus on the PLS (Partial Least Squares) analysis method. The PLS method, implemented through the SmartPLS application, synergizes partial path analysis with partial least squares techniques and has gained prominence as a preferred method for analyzing complex structural models within the field of Data Science. This study delves into the practical applications and benefits of PLS in handling diverse and intricate datasets, and also elucidates the potential obstacles encountered during its implementation. By examining the methodological strengths and addressing the challenges associated with PLS, this paper aims to provide valuable insights for researchers and practitioners seeking to leverage this method and the SmartPLS application for enhanced data analysis and informed decision-making.
AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments Abas Sunarya; Sunarjo, Richard Andre; Abbas, Maulana; Al-Kamari, Omar Arif; Sabda Maulana
International Transactions on Education Technology (ITEE) Vol. 4 No. 1 (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.v4i1.950

Abstract

The rapid growth of artificial intelligence in higher education creates new op- portunities to make learning factory environments more adaptive, data-informed, and aligned with industrial practice. This study examines how the integration of educational data analytics and intelligent tutoring systems supports smarter learning factory models that connect theoretical instruction with hands-on indus- trial training. Using a quantitative research design, data were collected from 180 higher education students participating in AI-supported learning factory sessions. Log data on learning interactions, performance metrics, and system- generated feedback were analyzed using statistical modeling to test the effects of AI-driven interventions on learning outcomes. The results show that ed- ucational data analytics significantly increases the adaptability of instructional content, enabling the intelligent tutoring system to personalize learning paths in real time based on individual performance profiles. Students who engaged with AI-based tutoring reported higher learning engagement and achieved better problem-solving scores and stronger retention of practical concepts than those in conventional learning factory settings. These findings indicate that combining educational data analytics with intelligent tutoring systems improves both the efficiency and effectiveness of learning factory models by enabling continuous feedback loops, dynamic adjustment of learning tasks, and learner-centered in- struction. The study concludes that AI-driven, data-informed learning factories can play a strategic role in preparing students with industry-relevant compe- tences and offers practical implications for educational technologists and insti- tutions designing next-generation education technology solutions.
Co-Authors Abas Sunarya Abbas, Maulana Br. Karo, Mestiana Miller, Bella Pertiwi Sabda Maulana Sunarjo, Richard Andre