The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 493–499, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-493-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 493–499, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-493-2022
 
02 Jun 2022
02 Jun 2022

WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM

F. Chen, C. Jing, H. Zhang, and X. Lv F. Chen et al.
  • School of Geomatis and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Keywords: WiFi, Geospatial Dashboard, Behavior Analysis, College Students, Flow Forecast, Visualization System

Abstract. Student behavior research can improve learning efficiency, provide decision evidences for infrastructure management. Existing campus-scale behavioral analysis work have not taken into account the students characteristics and spatiotemporal pattern. Moreover, the visualization methods are weak in wholeness, intuitiveness and interactivity perspectives. In this paper, we design a geospatial dashboard-based student behavior analysis and visualization system considering students characteristics and spatiotemporal pattern. This system includes four components: user monitoring, data mining analysis, behavior prediction and spatiotemporal visualization. Furthermore, a deep learning model based on LSTNet to predict student behaviour. Our work takes WiFi log data of a university in Beijing as dataset. The results show that this system can identify student behavior patterns at a finer granularity by visualization method, which is helpful in improving learning and living efficiency.