Predicting students' performance using artificial neural networks Print
I.E. Livieris, K. Drakopoulou and P. Pintelas, Predicting students' performance using artificial neural networks, In the Proceddings of Information and Communication Technologies in Education, 2012.

Abstract - Artificial intelligence has enabled the development of more sophisticated and more efficient student models which represent and detect a broader range of student behavior than was previously possible. In this work, we describe the implementation of a user-friendly software tool for predicting the students' performance in the course of “Mathematics” which is based on a neural network classifier. This tool has a simple interface and can be used by an educator for classifying students and distinguishing students with low achievements or weak students who are likely to have low achievements.