EduNet: A Deep Neural Network Approach for Predicting CGPA of Undergraduate Students

Educational Data Mining (EDM) is an emerging research field concerned with the application of data mining, machine learning, and statistics in the discipline of education. Many researchers have already focused on EDM and exploring the educational data using several traditional data mining techniques to improve the educational performance of the students by extracting the concealed patterns and predicting the final outcome. In this study, we aim to propose a Deep Neural Network (DNN) based model to predict the final CGPA of the undergraduate business students with a minimal error than the traditional approaches. We have considered the performance of a decision tree model as the baseline performance. Experiments in this study have shown that our proposed DNN model can predict the CGPA with a significantly minimal error rate. To measure the performance of our model we have considered the three evaluation metrics namely Mean Squared Error (=0.008), Mean Absolute Error (=0.067), and Mean Absolute Percentage Error (=2.074). Our proposed model has successfully shown a promising prediction performance by reducing the MSE, MAE, and MAPE by 0.0146, 0.0431, and 6.043 respectively, compared to the baseline model.