Characterizing Social Interactions in Online Social Networks: The Case of University Students

M. E. Sousa-Vieira, J. C. López-Ardao, M. Fernández-Veiga

Abstract

The widespread use of computing and communications technologies has enabled the popularity of social networks oriented to learn. In a previous work, we studied the nature and strength of associations between undergraduate students of an introductory course on computer networks, using an online social network embedded in a learning management system. With datasets from two offerings of the same course, we mined the sequences of questions and answers posted by the students to identify structural properties of the social graph, patterns of collaboration among students and factors influencing the final achievements, concluding that the structural properties most correlated to the final academic results are robust measures of centrality (degree and eigenvector), which are already detectable since the first weeks of the course. In this work, we apply SNA to graduate engineering students enrolled in a master level course in computer networks. The results obtained show that quality participation in the social activities appears to be correlated with the final outcome of the course, and that good students tend to show denser egonetworks. Our analysis contributes to the understanding of the role of social learning among highly educated students.

References

  1. Cadima, R., Ojeda, J., and Monguet, J. (2012). Social networks and performance in distributed learning communities. Educational Technology & Society, 15(4):296-304.
  2. Chung, K. and Paredes, W. (2015). Towards a social networks model for online learning & performance. Educational Technology & Society, 18(3):240-253.
  3. Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3):224-238.
  4. Eid, M. and Al-Jabri, I. (2016). Social networking, knowledge sharing and student learning: The case of university students. Computers & Education, 99:14-27.
  5. Gaggioli, A., Mazzoni, E., Milani, L., and Riva, G. (2015). The creative link: Investigating the relationship between social network indices, creative performance and flow in blended teams. Computers in Human Behavior, 42(1):157-166.
  6. Hart, J. (2011). Social learning handbook. Learning and Performance Technologies.
  7. Hommes, J., Rienties, B., Grave, W., Bos, G., Schuwirth, L., and Scherpbier, A. (2012). Visualising the invisible: A network approach to reveal the informal social side of student learning. Advances in Health Sciences Education, 17(5):743-757.
  8. Lykourentzou, I., Giannoukos, I., Nikolopoulos, V., Mpardis, G., and Loumos, V. (2009). Dropout prediction in e-learning courses through the combination of machine learning techniques. Computers & Education, 53(3):950-965.
  9. Macfadyen, L. and Dawson, S. (2010). Mining LMS data to develop an ”early warning system” for educators: A proof of concept. Computers & Education, 54(2):588-599.
  10. Newman, M. (2010). Networks: An Introduction. Oxford University Press.
  11. Rodrigues, J., Sabino, F., and Zhou, L. (2011). Enhancing elearning experience with online social networks. IET Communications, 5(8):1147-1154.
  12. Skrypnyk, O., Joksimovic, S., Kovanovic, V., Gasevic, D., and Dawson, S. (2015). Roles of course facilitators, learners and technology in the flow of information of a cMOOC. International Review of Research in Online and Distance Learning, 16(3):743-757.
  13. Sousa, E., L ópez, J., Fernández, M., Rodríguez, M., and Herrería, S. (2016). An open-source platform for using gamification and social learning methodologies in engineering education: Design and experience. Computer Applications in Engineering Education.
  14. Sousa, E., L ópez, J., Fernández, M., Rodríguez, M., and L ópez, C. (2015). Mining relations in learningoriented social networks. In DSAA'15, 2nd. IEEE International Conference on Data Science and Advanced Analytics.
  15. Thoms, B. (2011). A dynamic social feedback system to support learning and social interaction in higher education. IEEE Transactions on Learning Technologies, 4(4):340-352.
  16. Vassileva, J. (2008). Toward social learning environments. IEEE Transactions on Learning Technology, 1(4):199-214.
Download


Paper Citation


in Harvard Style

Sousa-Vieira M., López-Ardao J. and Fernández-Veiga M. (2017). Characterizing Social Interactions in Online Social Networks: The Case of University Students . In Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU, ISBN 978-989-758-240-0, pages 188-199. DOI: 10.5220/0006292701880199


in Bibtex Style

@conference{csedu17,
author={M. E. Sousa-Vieira and J. C. López-Ardao and M. Fernández-Veiga},
title={Characterizing Social Interactions in Online Social Networks: The Case of University Students},
booktitle={Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU,},
year={2017},
pages={188-199},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006292701880199},
isbn={978-989-758-240-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Supported Education - Volume 2: CSEDU,
TI - Characterizing Social Interactions in Online Social Networks: The Case of University Students
SN - 978-989-758-240-0
AU - Sousa-Vieira M.
AU - López-Ardao J.
AU - Fernández-Veiga M.
PY - 2017
SP - 188
EP - 199
DO - 10.5220/0006292701880199