Research on student behavior characters from the campus big data

Investigating behavioral characteristics of college students is of great importance to grasp students' behaviors and assist administrators make efficient manage strategies. In this paper, our research focuses on the discovery of burst nature and memory effect of selecting behavior and repeating behavior, which are universal characteristics of human beings, from the campus big data. To this end, we firstly analysis the interevent time distributions of selecting canteens and entering the library, respectively. Results show that both of these behaviors significantly follow heavy-tail power-law distribution, which verify the preference of these behaviors. Then, burstiness parameter B and memory M are measured based on interevent time series, and we observe that the corresponding distributions approximately follow Gaussian forms. Furthermore, we analyze the relations between students' GPA and B or M. The main results indicate that GPA not only decays with the value of burst of entering the library, but also is directly proportional to the absolute value of memory of selecting canteens.