Harnessing Machine Learning for Educational Insights: A Review and Comparative Analysis of Student Performance Prediction Models

Abstract

The prediction of student academic performance has gained significant attention in educational research, driven by the rapid advancements in machine learning (ML) techniques. This study provides a comprehensive review and comparative analysis of various ML algorithms employed in forecasting student outcomes. The integration of ML models in education facilitates early identification of at-risk students, enabling timely interventions to improve learning outcomes. Traditional statistical methods often fall short in capturing complex patterns within student data, whereas ML techniques such as Decision Trees, Support Vector Machines (SVM), Random Forest, Artificial Neural Networks (ANN), and Deep Learning models offer more robust and adaptive predictive capabilities. This paper systematically examines the strengths, limitations, and accuracy of these algorithms in diverse academic settings. Key performance metrics such as accuracy, precision, recall, and F1-score are analysed to evaluate the effectiveness of different ML models. Additionally, challenges such as data quality, feature selection, and ethical considerations in educational data mining are discussed. The review highlights the potential of ML-driven predictive models in transforming educational decision-making, enhancing personalized learning strategies, and fostering academic excellence. Future research directions are also proposed to optimize predictive frameworks for student performance assessment.