WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM
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 who...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-06-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2022/493/2022/isprs-archives-XLIII-B4-2022-493-2022.pdf |
| _version_ | 1818554511451488256 |
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| author | F. Chen C. Jing H. Zhang X. Lv |
| author_facet | F. Chen C. Jing H. Zhang X. Lv |
| author_sort | F. Chen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T09:40:38Z |
| format | Article |
| id | doaj.art-9d729854cfff44869860b7906a6e2c49 |
| institution | Directory Open Access Journal |
| issn | 1682-1750 2194-9034 |
| language | English |
| last_indexed | 2024-12-12T09:40:38Z |
| publishDate | 2022-06-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-9d729854cfff44869860b7906a6e2c492022-12-22T00:28:35ZengCopernicus PublicationsThe International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences1682-17502194-90342022-06-01XLIII-B4-202249349910.5194/isprs-archives-XLIII-B4-2022-493-2022WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEMF. Chen0C. Jing1H. Zhang2X. Lv3School of Geomatis and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatis and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatis and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaSchool of Geomatis and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, ChinaStudent 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.https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2022/493/2022/isprs-archives-XLIII-B4-2022-493-2022.pdf |
| spellingShingle | F. Chen C. Jing H. Zhang X. Lv WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM |
| title_full | WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM |
| title_fullStr | WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM |
| title_full_unstemmed | WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM |
| title_short | WIFI LOG-BASED STUDENT BEHAVIOR ANALYSIS AND VISUALIZATION SYSTEM |
| title_sort | wifi log based student behavior analysis and visualization system |
| url | https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B4-2022/493/2022/isprs-archives-XLIII-B4-2022-493-2022.pdf |
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