Abstract
The widespread use of computing and communications technologies has also arrived at the education field, an area where general and custom-designed online social networking software platforms are being increasingly employed in formal course offerings at all education levels. Despite the widespread use of open online social networks (OSN), the relationship between the social ties among students and their academic performance is not yet well understood. In this chapter, we report on a longitudinal experiment with a purpose-specific OSN run at the graduate level and apply a structural analysis of the students’ network. We show that some of the basic structural properties in these networks (e.g., centrality, community structure, reciprocity, egonetworks) are correlated with the final outcome and grades of the students. Thus, social network analysis during the instruction period can be effectively used to classify, rank and identify types of students according to the intensity, quality and engagement into the OSN. These features and the network structure are plausible variables to predict their ultimate academic achievements, too. Our analysis contributes to the understanding of the role of social learning among highly educated students.
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References
Barabasi, A.-L.: Network Science. Cambridge University Press, Cambridge (2016)
Cadima, R., Ojeda, J., Monguet, J.: Social networks and performance in distributed learning communities. Educ. Technol. Soc. 15(4), 296–304 (2012)
Cho, H., Gay, G., Davidson, B., Ingraffea, A.: Social networks, communication styles and learning performance in a CSCL community. Comput. Educ. 49(2), 309–329 (2007)
Chung, K., Paredes, W.: Towards a social networks model for online learning and performance. Educ. Technol. Soc. 18(3), 240–253 (2015)
Cross, J.: Informal learning: rediscovering the natural pathways that inspire innovation and performance. Pfeiffer, San Francisco (2006)
Dawson, S.: A study of the relationship between student social networks and sense of community. Educ. Technol. Soc. 11(3), 224–238 (2008)
Eid, M., Al-Jabri, I.: Social networking, knowledge sharing and student learning: the case of university students. Comput. Educ. 99, 14–27 (2016)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Gaggioli, A., Mazzoni, E., Milani, L., Riva, G.: The creative link: investigating the relationship between social network indices, creative performance and flow in blended teams. Comput. Hum. Behav. 42(1), 157–166 (2015)
Hart, J.: Social Learning Handbook. Centre for Learning and Performance Technologies, Bath (2011)
Haythornthwaite, C.: Learning relations and networks in web-based communities. J. Web Based Commun. 4(2), 140–158 (2008)
Hommes, J., Rienties, B., Grave, W., Bos, G., Schuwirth, L., Scherpbier, A.: Visualising the invisible: a network approach to reveal the informal social side of student learning. Adv. Health Sci. Educ. 17(5), 743–757 (2012)
Laat, M., Lally, V., Lipponen, L., Simons, R.: Investigating patterns of interaction in networked learning and computer-supported collaborative learning: a role for social network analysis. Int. J. Comput.-Support. Collab. Learn. 2(1), 87–103 (2007)
Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., Loumos, V.: Dropout prediction in e-learning courses through the combination of machine learning techniques. Comput. Educ. 53(3), 950–965 (2009)
Macfadyen, L., Dawson, S.: Mining LMS data to develop an “early warning system” for educators: a proof of concept. Comput. Educ. 54(2), 588–599 (2010)
Newman, M.: Networks: An introduction. Oxford University Press, Oxford (2010)
Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. Graph Algorithms Appl. 10(2), 191–218 (2006)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74(1), 016110 (2006)
Reuven, A., Zippy, E., Gilad, R., Aviva, G.: Network analysis of knowledge construction in asynchronous learning networks. J. Asynchronous Learn. Netw. 7(3), 1–23 (2003)
Rodrigues, J., Sabino, F., Zhou, L.: Enhancing e-learning experience with online social networks. IET Commun. 5(8), 1147–1154 (2011)
Skrypnyk, O., Joksimovic, S., Kovanovic, V., Gasevic, D., Dawson, S.: Roles of course facilitators, learners and technology in the flow of information of a cMOOC. Int. Rev. Res. Online Distance Learn. 16(3), 743–757 (2015)
Sousa, M., López, J., Fernández, M.: Characterizing social interactions in online social networks: the case of university students. In: Proceedings of the 9th International Conference on Computer Supported Education (CSEDU 2017), vol. 2, pp. 188–199 (2017)
Sousa, M., López, J., Fernández, M., Rodríguez, M., Herrería, S.: An open-source platform for using gamification and social learning methodologies in engineering education: design and experience. Comput. Appl. Eng. Educ. 24(5), 813–826 (2016)
Sousa, M., López, J., Fernández, M., Rodríguez, M., López, C.: Mining relations in learning-oriented social networks. Comput. Appl. Eng. Educ. 25(5), 769–784 (2017)
Thoms, B.: A dynamic social feedback system to support learning and social interaction in higher education. IEEE Trans. Learn. Technol. 4(4), 340–352 (2011)
Vassileva, J.: Toward social learning environments. IEEE Trans. Learn. Technol. 1(4), 199–214 (2008)
Wasserman, S., Faust, K.: Social Networks Analysis: Methods and Applications. Cambridge University Press, Cambridge (1997)
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Sousa-Vieira, M.E., López-Ardao, J.C., Fernández-Veiga, M. (2018). The Network Structure of Interactions in Online Social Learning Environments. In: Escudeiro, P., Costagliola, G., Zvacek, S., Uhomoibhi, J., McLaren, B. (eds) Computers Supported Education. CSEDU 2017. Communications in Computer and Information Science, vol 865. Springer, Cham. https://doi.org/10.1007/978-3-319-94640-5_20
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