Article Details

Abstract

Good daily habits are essential for college students' success, and habits manifest as observable daily behaviors. How to utilize student activity data recorded by various sensors in a digital campus to uncover daily life habits and enable applications such as behavior prediction is an important approach for personalised student development. Starting from digital campus big data, this paper, based on the defined expression relationship among activity, behavior, and habit, proposes a three-dimensional spatial parallel coordinate analysis method. By performing periodic temporal analysis, spatial overlap analysis, and spatiotemporal joint correlation analysis, it derives activity sequences with cyclical repetition and consistent spatial overlap, establishes a habit visual expression based on three-dimensional spatiotemporal data, and validates the effectiveness of the method using actual semester data.

Keywords

college student; activity habit; parallel coordinate; visual analysis

References

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About the authors

Zeng Chaoyong (1998-), male, Han ethnicity, from Tianmen, Hubei, engineer, master's student, research direction: data mining; Corresponding author: Li Qianqian (2000-), female, Han ethnicity, from Yichun, Jiangxi. She is a laboratory technician and a master's student. Her research focuses on education big data analysis and data governance.