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Unveiling Academic Success: Harnessing Graph Machine Learning for Student Performance Prediction
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Author(s): Polisetty Sri Hari Sai Saran (Department of Data Science and Business Systems, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)and R. Krishna Kumari (Department of Mathematics, Faculty of Engineering and Technology, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, India)
Copyright: 2025
Pages: 26
Source title:
Practical Applications of Machine Learning and AI: Medicine, Environmental Science, Transportation, and Education
Source Author(s)/Editor(s): Toufik Mzili (Chouaib Doukkali University, Morocco)and Adarsh Kumar Arya (Harcourt Butler Technical University, India)
DOI: 10.4018/979-8-3373-1399-3.ch014
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Abstract
In this chapter, we delve into the utilization of graph machine learning techniques to forecast student academic performance. By harnessing graph-based representations of educational data, our study endeavors to unearth underlying patterns and connections that impact student success. Through a fusion of feature engineering, graph analytics, and predictive modeling, we aim to investigate the efficacy of graph-based methodologies in improving the precision and interpretability of student performance prediction systems. This paper investigates the effectiveness of logistic regression, K-nearest neighbors (KNN), and a custom Graph Neural Network (GNN) model for predicting student performance in exams. Our analysis reveals that the custom GNN model outperforms both logistic regression and KNN, achieving higher accuracy and efficiency in student performance prediction. The custom GNN model leverages the graph-based representation of educational data, which enhances its ability to capture complex relationships and dependencies among students.
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