The Effects of Non-Directional Online Behavior on Students’ Learning Performance: A User Profile Based Analysis Method

Abstract

Network behavior analysis is an effective method to outline user requirements, and can extract user characteristics by constructing machine learning models. To protect the privacy of data, the shared information in the model is limited to non-directional network behavior information, such as online duration, traffic, etc., which also hides users’ unconscious needs and habits. However, the value density of this type of information is low, and it is still unclear how much student performance is affected by online behavior; in addition there is a lack of methods for analyzing the correlation between non-directed online behavior and academic performance. In this article, we propose a model for analyzing the correlation between non-directed surfing behavior and academic performance based on user portraits. Different from the existing research, we mainly focus on the public student behavior information in the campus network system and conduct in-depth research on it. The experimental results show that online time and online traffic are negatively correlated with academic performance, respectively, and student’s academic performance can be predicted through the study of non-directional online behavior.