A student’s performance prediction method based on neural collaborative filtering

Abstract

Along with the increasingly fast development of elearning and the transformation of the traditional classroom, novel technology should be applied in human learning domain, that is called smart learning. Since deep learning has been quickly developed in recent years, a diversity of prediction methods have been successfully applied in many domains. Most of the recent studies about student’s performance prediction mainly use machine learning methods like decision tree and k-nearest neighbors to discover the correlation between the student’s features and performances. In this work, we explore the application of deep learning in student’s performance prediction scenario. Inspired by some works in which the combination of deep learning with collaborative filtering is explored, we propose a novel method based on neural collaborative filtering for student’s performance prediction which, unlike other recent works about student’s performance prediction, does not require outer features of students. The main contribution of our work is that we explore a novel representation method for latent features in which the latent space is separated on several parts by their meanings which allows the model to learn a better latent representation for inference. Our results for real student’s score prediction show that our proposed methods can outperform existing models.