Abstract
Learning community detection in social networks has an important role to understand and to analyze the network structure. The main objective of our study is to evaluate learning communities based on interactions between learners. To meet this goal, we propose a new algorithm called Community Detection and Evaluation Algorithm (EDCA). This algorithm detects learning communities using a new centrality measure named “safely centrality” that allows to detect safe learners. These learners represent the initial nodes of communities. Afterward, we identify neighbors of each safe learner to build communities. In order to test the performance of our method, we compare our proposed algorithm with three community detection algorithms in two learning networks using the modularity and the Adjusted Rand Index (ARI) metrics. Our experimental phase demonstrates the quality of our proposed algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
McConnell, E., et al.: “Everybody puts their whole life on Facebook”: identity management and the online social networks of LGBTQ youth. Int. J. Environ. Res. Public Health 15, 1078 (2018)
Stacey, E.: Social presence online: networking learners at a distance. In: Watson, D., Andersen, J. (eds.) Networking the Learner: Computers in Education, pp. 39–48 (2002)
Ferguson, R., Shum, S.B.: Social learning analytics: five approaches. In: Proceedings of the 2nd International Conference on Learning Analytics and Knowledge, pp. 23–33. ACM (2012)
Elyazid, A., Brahim, O., Bouchra, F.: A comparative study of some algorithms for detecting communities in social networks. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt), pp. 257–262 (2016)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Wang, Y., Di, Z., Fan, Y.: Identifying and characterizing nodes important to community structure using the spectrum of the graph. PLoS ONE 6, e27418 (2011)
Martin, F., Ndoye, A.: Using learning analytics to assess student learning in online courses. J. Univ. Teach. Learn. Pract. 13, 7 (2016)
Inderawati, R., Pratama, A.H., Loeneto, B.A.: Peer assessment in Facebook comment column about one topic in writing II subject of the fourth semester students of Sriwijaya University English study program. Engl. FRANCA Acad. J. Engl. Lang. Educ. 2, 49–72 (2018)
Harmon, O.R., Tomolonis, P.A.: The effects of using Facebook as a discussion forum in an online principles of economics course: Results of a randomized controlled trial. Int. Rev. Econ. Educ. 30, 100157 (2019)
Jokar, E., Mosleh, M.: Community detection in social networks based on improved Label Propagation Algorithm and balanced link density. Phys. Lett. A 383, 718–727 (2019)
Nikolaev, A.G., Razib, R., Kucheriya, A.: On efficient use of entropy centrality for social network analysis and community detection. Soc. Netw. 40, 154–162 (2015)
Li, X., et al.: Communities detection in social network based on local edge centrality. Phys. Stat. Mech. Appl. 531, 121552 (2019)
Adraoui, M., Retbi, A., Idrissi, M.K., Bennani, S.: Evaluate learning communities in the online social media: Facebook groups. In: Proceedings of the 12th International Conference on Intelligent Systems: Theories and Applications (2018)
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci. 105, 1118–1123 (2008)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74, 036104 (2006)
He, Z., Deng, S., Xu, X., Huang, J.Z.: A fast greedy algorithm for outlier mining. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 567–576. Springer, Heidelberg (2006)
Heidler, R., Gamper, M., Herz, A., Eßer, F.: Relationship patterns in the 19th century: the friendship network in a German boys’ school class from 1880 to 1881 revisited. Soc. Netw. 37, 1–13 (2014)
Martín, E., Gértrudix, M., Urquiza Fuentes, J., Haya, P.A.: Student activity and profile datasets from an online video-based collaborative learning experience. Br. J. Educ. Technol. 46, 993–998 (2015)
Labatut, V.: Generalized measures for the evaluation of community detection methods. Int. J. Soc. Netw. Min. 2, 44 (2015)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2, 193–218 (1985)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Adraoui, M., Retbi, A., Idrissi, M.K., Bennani, S. (2020). A New Approach to Detect At-Risk Learning Communities in Social Networks. In: Serrhini, M., Silva, C., Aljahdali, S. (eds) Innovation in Information Systems and Technologies to Support Learning Research. EMENA-ISTL 2019. Learning and Analytics in Intelligent Systems, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-030-36778-7_9
Download citation
DOI: https://doi.org/10.1007/978-3-030-36778-7_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36777-0
Online ISBN: 978-3-030-36778-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)