Student Success Prediction Based on Machine Learning and Learning Styles


Author: Dijana Oreški

Abstract: This study aims to develop a predictive model for student success by integrating machine learning algorithms with learning style analysis. Educational institutions increasingly recognize the value of early performance prediction to implement timely interventions and enhance learning outcomes. Learning management systems generate vast amounts of data. The proposed research will analyze student interaction data from Moodle learning management system, including course logins, resource access patterns, assignment submissions, and assessment performance. These digital footprints will be combined with learning style assessments to identify patterns of academic achievement. Machine learning algorithms are applied and compared to determine the most effective predictive model. This research contributes to educational data mining by exploring the intersection between digital behavior patterns, individual learning preferences, and academic outcomes. The resulting model achieves high prediction accuracy, enabling proactive educational interventions that adapt to students' learning styles while leveraging Moodle's AI capabilities for personalized learning experiences.

Pages: 96-99

DOI: 10.46300/9109.2025.19.10

International Journal of Education and Information Technologies, E-ISSN: 2074-1316, Volume 19, 2025, Art. #10

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