A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks

Ann Nosseir, Yahia Fathy


Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the students’ performance during their study years. This helps tailoring an appropriate learning strategy for different groups.

This work develops a novel framework for a mobile app to predict the students’ performance before starting the Universities’ education. The framework is built on a University’s students data from year 2009-2017. It has three main components, namely, a neural network model that predicts the GPA, a mobile App that tests basic knowledge in different domains, and a fuzzy model that estimates the future students’ performance. 


A mobile App, Predictive systems, Fuzzy algorithm, Neural Network, Peda-gogy

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International Journal of Interactive Mobile Technologies (iJIM) – eISSN: 1865-7923
Creative Commons License
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