Exploring the association between mobility behaviours and academic performances of students: a context-aware traj-graph (CTG) analysis
Tóm tắt
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.
Tài liệu tham khảo
AndroSensor. https://play.google.com/store/apps/details?id=com.fivasim.androsensor&hl=en_IN
Alessandretti, L., Lehmann, S., Baronchelli, A.: Individual mobility and social behaviour: two sides of the same coin (2018). arXiv preprint arXiv:1801.03962
Barlacchi, G., Perentis, C., Mehrotra, A., Musolesi, M., Lepri, B.: Are you getting sick? Predicting influenza-like symptoms using human mobility behaviors. EPJ Data Sci. 6(1), 27 (2017)
Bunke, H.: On a relation between graph edit distance and maximum common subgraph. Pattern Recogn. Lett. 18(8), 689–694 (1997)
Canzian, L., Musolesi, M.: Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis, pp. 1293–1304 (2015)
Cao, Y., Gao, J., Lian, D., Rong, Z., Shi, J., Wang, Q., Wu, Y., Yao, H., Zhou, T.: Orderness predicts academic performance: behavioral analysis on campus lifestyle (2017). arXiv preprint arXiv:1704.04103
Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. IJCAI 13, 2605–2611 (2013)
Du, B., Liu, C., Zhou, W., Hou, Z., Xiong, H.: Catch me if you can: detecting pickpocket suspects from large-scale transit records. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 87–96. ACM (2016)
Furnham, A., Chamorro-Premuzic, T., McDougall, F.: Personality, cognitive ability, and beliefs about intelligence as predictors of academic performance. Learn. Individ. Differ. 14(1), 47–64 (2003)
Ghosh, S., Ghosh, S.K.: Thump: semantic analysis on trajectory traces to explore human movement pattern. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 35–36. International World Wide Web Conferences Steering Committee (2016)
Ghosh, S., Ghosh, S.K.: Exploring human movement behaviour based on mobility association rule mining of trajectory traces. In: Proceedings of the 17th International Conference on Intelligent Systems Design and Applications, pp. 451–463. Springer, Berlin (2017)
Ghosh, S., Ghosh, S.K.: Modeling of human movement behavioral knowledge from gps traces for categorizing mobile users. In: Proceedings of the 26th International Conference on World Wide Web Companion, pp. 51–58. International World Wide Web Conferences Steering Committee (2017)
Kassarnig, V., Bjerre-Nielsen, A., Mones, E., Lehmann, S., Lassen, D.D.: Class attendance, peer similarity, and academic performance in a large field study. PloS ONE 12(11), e0187078 (2017)
Kassarnig, V., Mones, E., Bjerre-Nielsen, A., Sapiezynski, P., Dreyer Lassen, D., Lehmann, S.: Academic performance and behavioral patterns. EPJ Data Sci. 7, 1–16 (2018)
Krakiwsky, E.J., Harris, C.B., Wong, R.V.C.: A kalman filter for integrating dead reckoning, map matching and gps positioning. In: Position Location and Navigation Symposium, 1988. Record. Navigation into the 21st Century. IEEE PLANS’88., IEEE, pp. 39–46. IEEE (1988)
Likhyani, A., Padmanabhan, D., Bedathur, S., Mehta, S.: Inferring and exploiting categories for next location prediction. In: Proceedings of the 24th International Conference on World Wide Web, pp. 65–66. ACM (2015)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200 (2016)
Lv, M., Chen, L., Chen, G.: Discovering personally semantic places from gps trajectories. In: Proceedings of the 21st ACM international conference on Information and knowledge management, pp. 1552–1556. ACM (2012)
Michail, O.: An introduction to temporal graphs: an algorithmic perspective. Internet Math. 12(4), 239–280 (2016)
Nghe, N.T., Janecek, P., Haddawy, P.: A comparative analysis of techniques for predicting academic performance. In: Frontiers in Education Conference-Global Engineering: Knowledge Without Borders, Opportunities Without Passports, 2007. FIE’07. 37th Annual, pp. T2G–7. IEEE (2007)
Pan, G., Qi, G., Zhaohui, W., Zhang, D., Li, S.: Land-use classification using taxi GPS traces. IEEE Trans. Intell. Transp. Syst. 14(1), 113–123 (2013)
POI Hierarchy. https://developer.foursquare.com/docs/resources/categories/. Accessed Nov 2017
Song, C., Zehui, Q., Blumm, N., Barabási, A.-L.: Limits of predictability in human mobility. Science 327(5968), 1018–1021 (2010)
Timóteo, I.J.P.M., Araújo, M.R., Rossetti, R.J.F., Oliveira, E.C.: Using trasmapi for the assessment of multi-agent traffic management solutions. Prog. Artif. Intell. 1(2), 157–164 (2012)
Vandamme, J.-P., Meskens, N., Superby, J.-F.: Predicting academic performance by data mining methods. Educ. Econ. 15(4), 405–419 (2007)
Vázquez, M.-R., Romero, F.P., Olivas, J.A., Orbe, E., Serrano-Guerrero, J.: An approach to academic performance prediction in tutoring systems based on fuzzy deformable prototypes. Prog. Artif. Intell. 5(1), 55–64 (2016)
Vlachos, M., Kollios, G., Gunopulos, D.: Discovering similar multidimensional trajectories. In: 18th International Conference on Data Engineering, 2002. Proceedings, pp. 673–684. IEEE (2002)
Wang, R., Harari, G., Hao, P., Zhou, X., Campbell, A.T.: Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 3–14. ACM (2014)
Wang, R., Harari, G., Hao, P., Zhou, X., Campbell, A.T.: Smartgpa: how smartphones can assess and predict academic performance of college students. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 295–306. ACM (2015)
Wasserman, L.: All of Statistics: A Concise Course in Statistical Inference. Springer, Berlin (2013)
Yao, D., Zhang, C., Huang, J., Bi, J.: Serm: a recurrent model for next location prediction in semantic trajectories. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 2411–2414. ACM (2017)
Ying, J.J.-C., Lee, W.-C., Weng, T.-C., Tseng, V.S.: Semantic trajectory mining for location prediction. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 34–43. ACM (2011)
Yuan, J., Zheng, Y., Xie, X.: Discovering regions of different functions in a city using human mobility and pois. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 186–194. ACM (2012)
Zheng, Y.: Trajectory data mining: an overview. ACM Trans. Intell. Syst. Technol. (TIST) 6(3), 29 (2015)
Zheng, Y., Zhang, L., Ma, Z., Xie, X., Ma, W.-Y.: Recommending friends and locations based on individual location history. ACM Trans. Web (TWEB) 5(1), 5 (2011)
Zheng, Y., Zhang, L., Xie, X., Ma, W.-Y.: Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, pp. 791–800. ACM (2009)