Using Machine Learning to Predict and Improve Student Learning Outcomes in Real Time

Abstract

The integration of machine learning (ML) into educational systems has the potential to revolutionize how student learning outcomes are predicted and enhanced in real time. This paper explores the application of various ML algorithms to analyze and interpret vast amounts of educational data, aiming to identify patterns and predictors of student performance. By leveraging real-time data analytics, educators can implement timely interventions tailored to individual student needs, thereby improving overall academic achievement. The study reviews existing literature on ML in education, outlines a comprehensive methodology for data collection and analysis, presents findings from experimental implementations, and discusses the implications for future educational practices. The results indicate that ML models, particularly ensemble methods and neural networks, exhibit high accuracy in predicting student outcomes and offer actionable insights for personalized learning strategies.