Convolutional Neural Network for Predicting Student Academic Performance in Intelligent Tutoring System

Abstract

One of the most significant research areas in education and Artificial Intelligence (AI) is the earlier prediction of students’ academic achievement. Limited studies have been conducted using Deep Learning (DL) in the student domain of Intelligent Tutoring System (ITS). Traditional Machine Learning (ML) techniques have been employed in many earlier publications to predict student performance. This paper investigates the effectiveness of DL algorithms for predicting student academic performance. Three different DL architectures based on the structure of Convolutional Neural Networks (CNN) are presented. Two public datasets are used. Furthermore, two feature selection techniques are utilized in this experiment: Principal Component Analysis (PCA) and Decision Trees (DTs). Also, we applied a resampling technique for the first dataset to address the issue of an imbalanced dataset. According to the experimental findings, the proposed CNN model’s success in predicting student performance at early stages reached an accuracy of 94.36% using the first dataset and 84.83% using the second dataset. Comparing the proposed approach with the previous studies, the proposed approach outperformed all previous studies when dataset 2 and part of dataset 1 were used. For the complete dataset 1, the proposed model performed very well.