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All Journal International Journal of Educational Technology and Society
Imelda Hutabarat
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Computer Science & IT Education Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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Adaptive Learning Analytics for Tracking Student Performance and Predicting Academic Success in Digital Classrooms Sri Suharti; Imelda Hutabarat; Danellie C. Llamas
International Journal of Educational Technology and Society Vol. 1 No. 3 (2024): September : International Journal of Educational Technology and Society
Publisher : Asosiasi Periset Bahasa Sastra Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/ijets.v1i3.411

Abstract

This research focuses on the application of predictive analytics in digital classrooms to track and predict student performance. The study aims to address the limitations of traditional teacher judgment, which often relies on limited data points and subjective assessments. The research proposes a machine learning-driven approach that utilizes data from Learning Management Systems (LMS), including student engagement, academic performance, and attendance, to predict student success or failure with greater accuracy. Various machine learning techniques, such as Support Vector Machine (SVM) and Random Forest (RF), are applied to develop a predictive model that can identify at-risk students early. The findings show that the model achieves an accuracy rate of over 85%, with key predictors including past academic performance and student engagement. This model outperforms traditional assessment methods by providing real-time, data-driven insights that enable timely interventions. The study concludes that predictive analytics has significant potential to enhance educational outcomes by offering personalized support and improving curriculum design. However, challenges such as data integration, fairness, and privacy concerns must be addressed for broader implementation.
Co-Authors Danellie C. Llamas Suharti, Sri