Prediction of student academic progression: A case study on Vignan University

Abstract

To explore the academic progression of the students, higher educational institutions need better assessment and prediction tools. In this regard, Multilayer Perceptron (MLP) based prediction application is proposed to predict the Grade Point Average (GPA) of the Undergraduate students by the make use of student's Previous Academic History, Regularity, No. of Backlogs, Degree of Intelligence, Working Nature, Discipline, Social Activities and Grade. With this application it is possible to predict the student's data that who are at risk, and some proactive measures like extra classes & supporting material are offered to improve the academic progress of those students. To evaluate the performance of the proposed application, data has recorded from 134 third Year Computer Science Engineering Students of Vignan University and achieved 95.52% and 97.37% of prediction accuracy with RBF and MLP respectively.