EXPLORING THE SYNERGY OF AI AND LEARNING ANALYTICS: A NEW PARADIGM FOR PERSONALIZED LEARNING IN DISTANCE EDUCATION

Abstract

This article discusses the application of machine learning for the personalization of early semester students in the context of Distance Education (PJJ) at the Open University (UT). UT students, who number more than half a million, are spread throughout Indonesia and have a high diversity. For this reason, learning solutions that effectively involve students and improve academic performance are needed. Along with the development of Big Data technology, Artificial Intelligence (AI) with Learning Analytics has emerged as a promising approach to increase student engagement in learning and learning outcomes. The research methodology involves the collection, pre-processing, and process of Moodle log data as a Learning Management System (LMS) used at UT, as well as student grade data from the Academic Information System. Python-based machine learning classification algorithms are used to identify patterns and predict student learning outcomes. Key features such as components, activities, and target students during the online tutorial, the number of access to each tutorial session, participation in discussions, and the results of the tutorial assessment were used to train the mechine learning model. The findings show that the model can accurately predict student success classes and provide tailored recommendations to improve learning outcomes with f, this study highlights the potential of AI-based Learning Analytics to create a more personalized and effective learning environment in Distance Education. The research is expected to contribute to how to use a data-based approach/big data to identify a large number of PJJ personalization both in quantitative and qualitative terms.  This research also underlines the importance of adopting innovative technology to overcome the challenges of PJJ student engagement and performance in order to create more effective and efficient learning strategies.