Prediction of Students' Academic Performance Based on Courses' Grades Using Deep Neural Networks

Predicting students' academic performance at an early stage of a semester is one of the most crucial research topics in the field of Educational Data Mining (EDM). Students are facing various difficulties in courses like "Programming" and "Data Structures" through undergraduate programs, which is why failure and dropout rates in these courses are high. Therefore, EDM is used to analyze students' data gathered from various educational settings to predict students' academic performance, which would help them to achieve better results in their future courses. The main goal of this paper is to explore the efficiency of deep learning in the field of EDM, especially in predicting students' academic performance, to identify students at risk of failure. A dataset collected from a public 4-year university was used in this study to develop predictive models to predict students' academic performance of upcoming courses given their grades in the previous courses of the first academic year using a deep neural network (DNN), decision tree, random forest, gradient boosting, logistic regression, support vector classifier, and K-nearest neighbor. In addition, we made a comparison between various resampling methods to solve the imbalanced dataset problem, such as SMOTE, ADASYN, ROS, and SMOTE-ENN. From the experimental results, it is observed that the proposed DNN model can predict students' performance in a data structure course and can also identify students at risk of failure at an early stage of a semester with an accuracy of 89%, which is higher than models like decision tree, logistic regression, support vector classifier, and K-nearest neighbor.