Polytechnic Students’ Academic Performance Prediction Based On Using Deep Neural Network
S. M. Abdullah Al Shuaeb *
Department of Computer Technology, Tangail Polytechnic Institute, Tangail, Directorate of Polytechnic Education, Bangladesh.
Shamsul Alam
Department of Computer Technology, Tangail Polytechnic Institute, Tangail, Directorate of Polytechnic Education, Bangladesh.
Md. Mizanur Rahman
Department of Computer Science and Engineering, Ranada Prasad Shaha University, Bangladesh.
Md. Abdul Matin
Begum Rokeaya Girls School and College, Gurudaspur, Natore, Bangladesh.
*Author to whom correspondence should be addressed.
Abstract
Students’ academic achievement plays a significant role in the polytechnic institute. It is an important task for the technical student to achieve good results. It becomes more challenging by virtue of the huge amount of data in the polytechnic student databases. Recently, the lack of monitoring of academic activities and their performance has not been harnessed. This is not a good way to evaluate the academic performance of polytechnic students in Bangladesh at present. The study on existing academic prediction systems is still not enough for the polytechnic institutions. Consequently, we have proposed a novel technique to improve student academic performance. In this study, we have used the deep neural network for predicting students' academic final marks. The main objective of this paper is to improve students' results. This paper also explains how the prediction deep neural network model can be used to recognize the most vital attributes in a student's academic data namely midterm_marks, class_ test, attendance, assignment, and target_ marks. By using the proposed model, we can more effectively improve polytechnic student achievement and success.
Keywords: Artificial Intelligence (AI), Artificial Neural Network (ANN), Deep Neural Network (DNN), Machine Learning (ML), Mean Squared Error (MSE).