Student Performance Prediction Using Machine Learning Techniques For Personalized Learning Paths

Authors: Nisha Rampravesh Gupta

Abstract: Machine Learning (ML) is revolutionizing the education sector by enabling data-driven insights into student performance and learning behaviors. One of its most promising applications is in the development of personalized learning paths that adapt to individual learners’ strengths, weaknesses, and preferences. However, predicting student performance involves multiple challenges, such as handling diverse data types, ensuring fairness, and maintaining transparency. This paper explores the role of ML in forecasting academic outcomes, reviews key prediction models, and evaluates their applicability in designing adaptive learning experiences. A framework is proposed that integrates predictive accuracy with explainability, ensuring that educational interventions are not only effective but also understandable by educators and students. The study emphasizes that ML-based performance prediction is a cornerstone of intelligent tutoring systems and a catalyst for inclusive, personalized education.

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