基于活动链分析的高校学生行为模式研究OA

In response to the demands of educational modernization,the scenario-based application of campus data provides new opportunities for the digital transformation of higher education.In order to identify spatiotemporal patterns of campus behavior,this study uses trajectory reconstruction,semantic mapping,and pattern mining on three months'worth of Wi-Fi logs and point of interest(POI)data from thirty thousand students and faculty.Innovatively introducing the Dirichlet multinomial regression(DMR)model and spatio-temporal routine mining on mobile phone data(STRMM)model for student behavior analysis,the DMR model effectively integrates dynamic trajectories and static landmark data to identify 10 categories of campus functional areas.The STRMM model enhances the ability to capture periodic and uncertain behaviors,categorizing undergraduate daily activities into 10 typical patterns,including standard teaching-oriented and focused research-oriented types.Additionally,grade-level dynamics in student behavior were shown by identifying six types of undergraduate groups with distinct evolutionary behavioral patterns.These groups demonstrated a shift from course-dominated activities in lower grades to self-directed research and flexible schedules in higher grades.The study confirms that Wi-Fi data-based behavioral analysis can effectively identify functional areas and student behavior characteristics,providing data support for precise management,resource optimization,and the practice of'Three-Comprehensive Education'with important practical reference value for promoting the digital transformation of higher education.