Specialized services and management must understand students’ behavioural patterns in a timely and accurate manner. Based on these patterns, we can make targeted rules, especially for unexpected patterns. To perform this type of work, a questionnaire method is usually used to collect data and analyse students’ behavioural states. However, the effectiveness of this method is greatly influenced by the timeliness and validity of the feedback data. To address this problem, we propose an unsupervised ensemble clustering framework to use student behavioural data to discover behavioural patterns. Because the behavioural data produced by students on campus are available in real time without intentional bias, clustering analysis can be relatively efficient and reliable. The proposed framework extracts behaviour features from the two perspectives of statistics and entropy and then combines density-based spatial clustering of applications with noise (DBSCAN) and k-means algorithms to discover behavioural patterns. To evaluate the performance of the proposed framework, we carry out experiments on six types of behavioural data produced by undergraduates in a university in Beijing and analyse the relationships between different behavioural patterns and students’ grade point averages (GPAs). The results show that the framework can not only detect anomalous behavioural patterns but also find mainstream patterns. The findings from this research can assist student-related departments in providing better services and management, such as psychological consulting and academic guidance
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