Abstract
Wide attention has been recently paid to academic performance prediction, due to its potentials of early warning and subsequent in-time intervention. However, there are few studies to consider the effect of social influence at predicting academic performance. The major challenge comes from the difficulty of collecting a precise friend list for students. To this end, we first construct students’ social relationship based on their campus behavior, and then predicts academic performance using constructed social network by semi-supervised learning. We evaluate the proposed algorithm on over 5,000 students with more than 14M behavior records. The evaluation results show the potential value of campus social network for predicting academic performance and the effectiveness of the proposed algorithm.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Cetintas, S., Si, L., Xin, Y.P., Tzur, R.: Probabilistic latent class models for predicting student performance. In: Proceedings of the 22nd ACM international conference on Conference on Information and Knowledge Management, pp. 1513–1516. ACM (2013)
Crandall, D.J., Backstrom, L., Cosley, D., Suri, S., Huttenlocher, D., Kleinberg, J.: Inferring social ties from geographic coincidences. Proc. Natl. Acad. Sci. 107(52), 22436–22441 (2010)
Eagle, N., Pentland, A.S., Lazer, D.: Inferring friendship network structure by using mobile phone data. Proc. Natl. Acad. Sci. 106(36), 15274–15278 (2009)
Fausett, L., Elwasif, W.: Predicting performance from test scores using backpropagation and counterpropagation. In: 1994 IEEE World Congress and IEEE International Conference on Computational Intelligence Neural Networks, vol. 5, pp. 3398–3402. IEEE (1994)
Friedland, L., Jensen, D., Lavine, M.: Copy or coincidence? A model for detecting social influence and duplication events. In: Proceedings of The 30th International Conference on Machine Learning, pp. 1175–1183 (2013)
Gao, H., Tang, J., Liu, H.: Exploring social-historical ties on location-based social networks. In: ICWSM (2012)
McPherson, M., Smith-Lovin, L., Cook, J.M.: Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27, 415–444 (2001)
Pham, H., Shahabi, C., Liu, Y.: EBM: an entropy-based model to infer social strength from spatiotemporal data. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, pp. 265–276. ACM (2013)
Sadilek, A., Kautz, H., Bigham, J.P.: Finding your friends and following them to where you are. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 723–732. ACM (2012)
Tamhane, A., Ikbal, S., Sengupta, B., Duggirala, M., Appleton, J.: Predicting student risks through longitudinal analysis. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1544–1552. ACM (2014)
Thai-Nghe, N., Drumond, L., Horváth, T., Schmidt-Thieme, L., et al.: Multi-relational factorization models for predicting student performance. In: KDD Workshop on Knowledge Discovery in Educational Data (KDDinED) (2011)
Wang, C., Ye, M., Lee, W.: From face-to-face gathering to social structure. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, pp. 465–474. ACM (2012)
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 (UbiComp). ACM (2015)
Wu, R., Liu, Q., Liu, Y., Chen, E., Su, Y., Chen, Z., Hu, G.: Cognitive modelling for predicting examinee performance. In: Proceedings of the 24th International Conference on Artificial Intelligence, pp. 1017–1024. AAAI Press (2015)
Yu, M., Si, W., Song, G., Li, Z., Yen, J.: Who were you talking to-mining interpersonal relationships from cellphone network data. In: 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 485–490. IEEE (2014)
Zafra, A., Romero, C., Ventura, S.: Multiple instance learning for classifying students in learning management systems. Expert Syst. Appl. 38(12), 15020–15031 (2011)
Acknowledgement
This work is supported by grants from the Natural Science Foundation of China (61502077, 61631005) and the Fundamental Research Funds for the Central Universities (ZYGX2014Z012).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yao, H., Nie, M., Su, H., Xia, H., Lian, D. (2017). Predicting Academic Performance via Semi-supervised Learning with Constructed Campus Social Network. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_37
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)