Early Prediction on Student Performance Using Machine Learning

Abstract

Predicting student academic outcomes through automated systems has become increasingly crucial due to the growing volume of data maintained by educational institutions This challenge is being tackled by the field of educational data mining (EDM), This issue is being addressed through educational data mining (EDM), a field that develops techniques to derive meaningful insights from such data to better understand students and their learning environments. Educational institutions frequently aim to estimate the number of students who will pass or fail to make informed preparations. Although many previous studies have focused on choosing the best classification algorithms, they often neglect the practical challenges of the data mining process, including high dimensionality, class imbalance, and misclassification issues, which can hinder model accuracy. Anticipating student academic outcomes is essential in designing effective educational strategies. Machine learning (ML) models can analyze historical academic data to identify patterns that predict student performance. This paper explores the application of various ML algorithms—such as Decision Trees, Support Vector Machines (SVM), Naive Bayes, and K-Nearest Neighbors (KNN)—to forecast student success rates. The goal is to enable educational institutions to intervene early, providing support where necessary to improve learning outcomes.