Student Behavior Pattern Mining and Analysis: Towards Smart Campuses.

Understanding student behavior patterns is fundamental to building smart campuses. However, the diversity of student behavior and the complexity of educational data not only bring great obstacles to the relevant research, but also leads to unstable performance and low reliability of current student behavior analysis systems. The emergence of educational big data and the latest advances in deep learning and representation learning provide unprecedented opportunities to tackle the above problems. In this talk, we introduce how we mine and analyze student behavior patterns by overcoming the complexity of educational data. Specifically, we propose a series of algorithmic frameworks, which take advantage of network science, data mining, and machine learning to form a data-driven system for mining and analyzing student behavior patterns. Our research not only fills the gap in the field of student abnormal behavior warning and student status monitoring, but also provides insights into data-driven smart city construction.