  Poor sleep habits may cause serious problems of mind and body, and it is a
commonly observed issue for college students due to study workload as well as
peer and social influence. Understanding its impact and identifying students
with poor sleep habits matters a lot in educational management. Most of the
current research is either based on self-reports and questionnaires, suffering
from a small sample size and social desirability bias, or the methods used are
not suitable for the education system. In this paper, we develop a general
data-driven method for identifying students' sleep patterns according to their
Internet access pattern stored in the education management system and explore
its influence from various aspects. First, we design a Possion-based
probabilistic mixture model to cluster students according to the distribution
of bedtime and identify students who are used to staying up late. Second, we
profile students from five aspects (including eight dimensions) based on
campus-behavior data and build Bayesian networks to explore the relationship
between behavioral characteristics and sleeping habits. Finally, we test the
predictability of sleeping habits. This paper not only contributes to the
understanding of student sleep from a cognitive and behavioral perspective but
also presents a new approach that provides an effective framework for various
educational institutions to detect the sleeping patterns of students.
