Students' Performance Prediction Using Machine Learning Based on Generative Adversarial Network

Abstract

Predicting student performance is a crucial area of research in the field of education. To improve the accuracy and reliability of student performance prediction, machine learning (ML) techniques have been widely used. In this study, we propose a novel approach for predicting student performance using five ML techniques, which include data analysis, pre-processing techniques, and data augmentation using GAN. We evaluate the proposed approach using a real-world dataset of student academic records and compare the results to those obtained without data augmentation. Our findings demonstrate that data augmentation significantly improves the accuracy and reliability of student performance prediction. Specifically, the random forest classifier achieves the best accuracy of 99.8%. This research contributes to the field of education by providing a more comprehensive and accurate model for predicting student performance, which can support informed decision-making and improve educational outcomes.