Prediction of academic performance associated with internet usage behaviors using machine learning algorithms

Abstract

College students are facilitated with increasingly convenient access to the Internet, which has a civilizing influence on students' learning and living. This study attempts to reveal the association between Internet usage behaviors and academic performance, and to predict undergraduate's academic performance from the usage data by machine learning. A set of features, including online duration, Internet traffic volume, and connection frequency, were extracted, calculated and normalized from the real Internet usage data of 4000 students. Three common machine learning algorithms of decision tree, neural network and support vector machine were used to predict academic performance from these features. The results indicate that behavior discipline plays a vital role in academic success. Internet connection frequency features are positively correlated with academic performance, whereas Internet traffic volume features are negatively associated with academic performance. From the perspective of the online time features, Internet time consumed results in unexpected performance between different datasets. Furthermore, as the number of features increase, prediction accuracy is generally improved in the methods. The results show that Internet usage data are capable of differentiating and predicting student's academic performance.