School of Engineering, Information Technology and Physical Sciences

Understanding and predicting abnormal behaviors of university students

PhD Project 

Project title

Understanding and predicting abnormal behaviors of university students

Project outline:

Detecting abnormal behaviours of university students in advance and providing guidance are one of the main tasks of higher education institutions. In addition to personal habits, abnormal university behaviours like drinking, drugs and even suicide are also closely related to undesirable social relationships, which has been demonstrated in previous research. Nevertheless, such an important feature can hardly be used in daily education management due to that it is not directly stored in the education management system. In this case, mining and analysing students' social networks not only contribute to understanding the causes of university abnormal behaviors but also provide a new perspective on its predictions. The research tasks of this PhD project mainly include: 1. To mine diverse social relationships accurately based on data stored in the education system, 2. To effectively integrate information from multiple social networks, 3. To accurately quantify and detect abnormal behaviours of students, and 4. To predict abnormal behaviours of students. On the whole, a theory of student social network mining will be proposed and a social network-based prediction framework will be designed to predict abnormal behaviours of university students.

Project supervision team:

Principle Supervisor: A/Prof Feng Xia

Associate Supervisor: Dr. Kathleen Keogh

Associate Supervisor: Dr. Giles Oatley