Paper Info
Title
Exploring the association between mobility behaviours and academic performances of students: a <Emphasis Type="Italic">context-aware traj-graph (CTG)</Emphasis> analysis
Abstract
Analysing the mobility traces of moving agents (mobile users, GPS-equipped vehicles, CDRs, etc.) may help in interpreting the “human interests and intentions” behind the movements and thus facilitates diverse range of location-based applications. The trajectory analysis uncovers the connections, correlations and differences among individuals and their activities by exploring their mobility attributes. This paper focuses on how mobility information (GPS traces) of a student exhibits correlation with her academic performance. The proposed framework analyses the GPS trajectories of students in an academic campus, models the mobility patterns of students using context-aware traj-graph (CTG), clusters signature mobility patterns and uncovers the correlation of mobility attributes with the academic performance of the students. A mobility knowledge graph has been constructed considering the entities, namely students, places of visits, movement behaviours, subjects, academic performances and the relationships among the entities. Using real-life dataset of an academic campus, we demonstrate that the mobility attributes are associated with students’ academic performances and students’ academic performance can be predicted from their movement behaviours.
Year
DOI
Venue
2018
10.1007/s13748-018-0164-6
Progress in Artificial Intelligence
Keywords
Field
DocType
Mobility, GPS trajectory, Academic performance, POI (point-of-interest), Movement pattern
Data science,Graph,Knowledge graph,Computer science,Correlation,Global Positioning System,Gps trajectory,Artificial intelligence,Trajectory analysis,Machine learning
Journal
Volume
Issue
ISSN
7
4
2192-6360
Citations 
PageRank 
References 
3
0.41
24
Authors
2
Name
Order
Citations
PageRank
Shreya Ghosh130.41
Soumya Kanti Ghosh234539.91