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All Journal Technomedia Journal ADI Bisnis Digital Interdisiplin (ABDI Jurnal) International Transactions on Education Technology (ITEE)
Abbas, Maulana
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Languange, Linguistic, Communication & Media Library & Information Science Social Sciences

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Journal : International Transactions on Education Technology (ITEE)

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The Impact of Educational Information Systems on Learning Accessibility in Higher Education Munthe, Rusli Ginting; Abbas, Maulana; Fernandez, Rico; Ulita, Novena
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.v3i1.686

Abstract

This study explores the impact of educational information systems on enhancing learning accessibility in higher education, as digital tools increasingly become integral to academic support, and student engagement. The main objective is to assess how these systems improve access to learning resources and facilitate communication, particularly for students from diverse backgrounds and with varying educational needs. Using a mixed-methods approach, this research combines quantitative analysis of accessibility metrics with qualitative insights from surveys and interviews with students and faculty across different higher education institutions. The findings show that educational information systems significantly enhance learning accessibility by providing flexible access to resources, facilitating real-time feedback, and supporting personalized learning paths. These systems also improve student engagement by enabling convenient access to materials and fostering a collaborative learning environment that accommodates different learning styles. However, the study identifies several barriers, including gaps in digital literacy, usability challenges, and unequal access to the necessary infrastructure, which can limit the effectiveness of these systems in reaching all students equally. Additionally, concerns around data privacy and system complexity are noted as areas needing attention to build user trust and ensure smoother system integration. The study concludes that while educational information systems hold great promise for improving accessibility and inclusivity in higher education, addressing these barriers through targeted training, digital equity initiatives, and robust data protection policies is essential for maximizing their potential. These insights offer valuable guidance for educational institutions aiming to create more inclusive learning environments through strategic integration of educational information systems.
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 Al-Kamari, Omar Arif Fernandez, Rico Fitra Putri Oganda Friandi, Sendy Zul Ikhsan, Ramiro Santiago Marviola Hardini Novena Ulita Po Abas Sunarya Rusli Ginting Munthe, Rusli Ginting Sabda Maulana Santoso, Nuke Puji Lestari Sihotang, Sondang Visiana Sunarjo, Richard Andre Xolani, Zanele