Machine Learning–Based Learning Analytics for Student Performance Prediction in Online Education: A Review

Abstract

Online education platforms and digital learning environments generate large volumes of data that can be effectively utilized to enhance teaching strategies and improve student learning outcomes. Machine learning–based learning analytics has emerged as a powerful approach for analyzing student interaction data and predicting academic performance in these environments. By examining patterns in learner engagement, behavioral activities, and assessment results, machine learning models can identify students at risk of poor performance and enable timely interventions. This review analyzes various algorithms such as logistic regression, decision trees, random forests, support vector machines, neural networks, and deep learning models used for performance prediction. It also highlights key data sources, including learning management system logs, assessment scores, forum participation, and engagement metrics. The findings suggest that machine learning techniques significantly improve prediction accuracy compared to traditional methods, enabling personalized learning pathways and better decision-making in educational institutions. However, challenges such as data privacy, ethical concerns, algorithmic bias, and model interpretability must be addressed. Overall, integrating machine learning with learning analytics can support adaptive learning systems and create more effective, data-driven educational environments.