A mobile application for early prediction of student performance using fuzzy logic and artificial neural networks

Abstract

Identifying students at risk, or potentially excellent students is increasingly important for higher education institutions to meet the needs of the students and to develop efficient learning strategies. Early stage prediction can give an indication of the students' performance during their study years. This helps to tailor an appropriate learning strategy for weak or excellent students. This work develops a novel framework for a mobile app to predict student performance before starting university education. The framework has three main components, namely, a neural network model that predicts GPA, a mobile app that tests basic knowledge in different domains, and a fuzzy model that estimates future student performance.