From Social Ties to Network Processes: Do Tie Definitions Matter?
The widespread adoption of digital e-learning environments and other learning technology has provided researchers with ready access to large quantities of data. Much of this data comes from discussion forums and has been studied with analytical methods drawn from social network analysis. However, within this large body of research there exists considerable variation in the definition of what constitutes a social tie, and the consequences of this choice are rarely described or examined. This paper presents findings from two distinct learning environments regarding different social tie extraction methods and their influence on the structural and statistical properties of the induced networks, and the association between measures of centrality and academic performance. Our findings indicate that social tie definitions play an important role in shaping the results of our analyses. The primary purpose of this paper is to raise awareness of the consequences that such methodological choices may have, and to promote transparency in future research.
Ba-Omar, H., Petrounias, I., & Anwar, F. (2007). A framework for using web usage mining to personalise e-learning. Proceedings of the 7th IEEE International Conference on Advanced Learning Technologies (ICALT ’07), 937–938. http://dx.doi.org/10.1109/ICALT.2007.13
Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and methods in social network analysis, 28. Cambridge University Press.
Cela, K., Sicilia, M., & Sánchez, S. (2015). Social Network Analysis in E-Learning Environments: A Preliminary Systematic Review. Educational Psychology Review, 27(1), 219–246. http://dx.doi.org/10.1007/s10648-014-9276-0
Cho, H., Gay, G., Davidson, B., & Ingraffea, A. (2007). Social networks, communication styles, and learning performance in a CSCL community. Computers & Education, 49(2), 309–329. http://dx.doi.org/10.1016/j.compedu.2005.07.003
Cobb, S. C. (2009). Social Presence and Online Learning: A Current View from a Research Perspective. Journal of Interactive Online Learning, 8(3), 241–254.
Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design and analysis issues for field settings. Chicago: Rand McNally.
Cramer, J. S. (2003). The standard multinomial logit model, in Logit Models from Economics and Other Fields, Cambridge University Press, 104–125. http://dx.doi.org/10.1017/CBO9780511615412
Croissant, Y., (2013). mlogit: multinomial logit model. http://dx.doi.org/10.18637/jss.v075.i03
Dado, M., & Bodemer, D. (2017). A review of methodological applications of social network analysis in computer-supported collaborative learning. Educational Research Review, 22, 159-180. http://dx.doi.org/10.1016/j.edurev.2017.08.005
Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology, 27(6), 924–942. http://dx.doi.org/10.14742/ajet.921
Dawson, S., Gašević, D., Siemens, G., & Joksimović S. (2014). Current state and future trends: A citation network analysis of the learning analytics field. Proceedings of the Fourth International Conference on Learning Analytics And Knowledge (LAK ‘14), 231–240. http://dx.doi.org/10/1145/2567574.2567585
Dawson, S. (2008). A Study of the Relationship between Student Social Networks and Sense of Community. Educational Technology & Society, 11(3), 224–238.
De Laat, M., & Prinsen, F. (2014), Social learning analytics: Navigating the changing settings of higher education. J. Res. Pract. Assess., 9(4),51–60.
N. Dowell, N., Skrypnyk, O., Joksimović, S., Graesser, A. C., Dawson, S., Gašević, D., de Vries, P., Hennis, T., & Kovanović, V. (2015). Modeling Learners’ Social Centrality and Performance through Language and Discourse. Proceedings of the 8th International Conference on Educational Data Mining. 250-257.
DuBois, C., Butts, C., & Smyth, P. (2013). Stochastic blockmodeling of relational event dynamics. Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics. 238–246.
Frank, O. D., & Strauss, D. (1986). Markov Graphs. Journal of the American Statistical Association. 81(395). 832–842. http://dx.doi.org/10.1080/01621459.1986.10478342
Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Soc. Netw. 1(3). 215–239. http://dx.doi.org/10.1016/0378-8733(78)90021-7
Gašević, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting learning success. The Internet and Higher Education, 28, 68–84. http://dx.doi.org/10.1016/j.iheduc.2015.10.002
Gašević, D., Kovanović, V., Joksimović, S., & Siemens, G. (2014). Where is research on massive open online courses headed? A data analysis of the MOOC Research Initiative. Int. Rev. Res. Open Distrib. Learn. 15(5). http://dx.doi.org/10.19173/irrodl.v15i5.1954
Gillani, N., & Eynon, R. (2014). Communication patterns in massively open online courses. The Internet and Higher Education. 23, 18-26. http://dx.doi.org/10.1016/j.iheduc.2014.05.004
Goodreau, S., Kitts, J., & Morris, M. (2009). Birds of a Feather, or Friend of a Friend? Using Exponential Random Graph Models to Investigate Adolescent Social Networks*. Demography. 46(1), 103–125. http://dx.doi.org/10.1353/dem.0.0045
Gruzd, A., & Haythornthwaite, C. (2008). Automated discovery and analysis of social networks from threaded discussions. International Sunbelt Social Network Conference.
Hoffb, P. D., Minhasa, S., & Warda, M. D. (2016). Inferential Approaches for Network Analysis: AMEN for Latent Factor Models.
Hunter, D. R., Handcock, M. S., Butts, C. T., Goodreau, S. M., & Morris M. (2008). ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks. J. Stat. Softw. 24(3), 1–29. http://dx.doi.org/10.18637/jss.v024.i03
Jiang, S., Fitzhugh, S. M., & Warschauer, M. (2014). Social Positioning and Performance in MOOCs. Proceedings of the Workshops held at Educational Data Mining, 7th International Conference on Educational Data Mining (EDM 2014). 1183, 14. http://dx.doi.org/10.1.1.662.8773
Joksimović, S., Dawson, S., Manataki, A., Kovanović, V., Gašević, D., & Friss de Kereki, I. (2016). Translating network position into performance: Importance of centrality in different network configurations. Proceedings of the Sixth International Conference on Learning Analytics And Knowledge (LAK ‘16), 314-323. http://dx.doi.org/10.1145/2883851.2883928
Jovanović, J., Gašević, D., Dawson, S., Pardo, A., & Mirriahi, N. (2017). Learning analytics to unveil learning strategies in a flipped classroom. The Internet and Higher Education, 33, 74–85. http://dx.doi.org/10.1016/j.iheduc.2017.02.001
Jordan, K. (2015). Synthesising MOOC completion rates | MoocMoocher. [Online]. Available: https://moocmoocher.wordpress.com/2013/02/13/synthesising-mooc-completion-rates/.
Kellogg, S., Booth, S., & Oliver, K. (2014). A social network perspective on peer supported learning in MOOCs for educators. The International Review of Research in Open and Distributed Learning. 15(5). http://dx.doi.org/10.19173/irrodl.v15i5.1852
Kovanović, V., Joksimović, S., Gašević, D., & Hatala, M. (2014). What is the source of social capital? The association between social network position and social presence in communities of inquiry. Proceedings of 7th International Conference on Educational Data Mining – Workshops (1st International Workshop on Graph-based Educational Data Mining (G-EDM)). http://dx.doi.org/10.1.1.664.4492
Kovanović, V., Gašević, D., Dawson, S., Joksimović, S., Baker, R. S., & Hatala, M. (2015). Does Time-on-task Estimation Matter? Implications for the Validity of Learning Analytics Findings. Journal of Learning Analytics. 2(3). 81-110. http://dx.doi.org/10.18608/jla.2015.23.6
Krackhardt, D. (1998). Super Strong and Sticky. Power Influ Organ. 21.
Krackhardt, D. (1999). The Ties that Torture: Simmelian Tie Analysis in Organizations. Res. Sociol. Organ., 16, 183–210.
Lage, M. J., Platt, G. J., & Tregua, M. (2000). Inverting the classroom: A gateway to creating an inclusive learning environment. The Journal of Economic Education. 31(1), 30–43. http://dx.doi.org/10.2307/1183338
Loevinger, J. (1957). Objective tests as instruments of psychological theory. Psychological Reports. 3. 635-694. http://dx.doi.org/10.2466/pr0.1918.104.22.1685
Lusher, D., Koskinen, J., & Robins G. (2012). Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Cambridge University Press.
Messick, S. (1994). Validity of Psychological Assessment: Validation of Inferences from Persons’ Responses and Performances as Scientific Inquiry into Score Meaning. ETS Research Report Series. 2. http://dx.doi.org/10.1002/j.2333-8504.1994.tb01634.x
Morris, M., Handcock, M. S., & Hunter D. R. (2008). Specification of Exponential-Family Random Graph Models: Terms and Computational Aspects. J. Stat. Softw. 24(4).
Munk, M., & Drlík, M. (2011). Impact of different pre-processing tasks on effective identification of users’ behavioral patterns in web-based educational system. Procedia Computer Science. 4, 1640–1649. http://dx.doi.org/10.1016/j.procs.2011.04.177
Nick, B., Lee, C.-K., Cunningham, P., & Brandes, U. (2013). Simmelian backbones: amplifying hidden homophily in facebook networks. Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference. 525–532. http://dx.doi.org/10.1145/2492517.2492569
Pardo, A., Jovanović, J., Dawson, S., & Gašević, D. (in press). Using Learning Analytics to Scale the Provision of Personalised Feedback. British Journal of Educational Technology.
Pardo, A., & Mirriahi, N. (2017). Design, Deployment and Evaluation of a Flipped Learning First-Year Engineering Course. Singapore: Springer Singapore. 177–191. http://dx.doi.org/10.1007/978-981-10-3413-8_11
Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students' satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402-417. http://dx.doi.org/10.1016/j.chb.2017.02.001
Robins G., Pattison P., Kalish Y., & Lusher D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks. 29(2), 173–191. http://dx.doi.org/10.1016/j.socnet.2006.08.002
Schreurs, B., Teplovs, C., Ferguson, R., de Laat, M., & Buckingham Shum, S. (2013). Visualizing Social Learning Ties by Type and Topic: Rationale and Concept Demonstrator. Proceedings of the Third International Conference on Learning Analytics and Knowledge (LAK ‘12). 33–37.
Scott, J. (2012). Social Network Analysis. SAGE Publications.
Shulman, L. S. (1970). Reconstruction of educational research. Review of Educational Research. 40, 371-396.
Skrypnyk, O., Joksimović, S., Kovanović, V., Gašević, D., & Dawson, S. (2015). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. Int. Rev. Res. Online Distance Learn.
Wang P., Robins G., Pattison P., & Lazega E. (2013). Exponential random graph models for multilevel networks. Social Networks. 35(1), 96–115. http://dx.doi.org/10.1016/j.socnet.2013.01.004
Wasserman S., & Pattison P. (1996). Logit models and logistic regressions for social networks: An Introduction to Markov graphs and p. Psychometrika. 61(3), 401–425. http://dx.doi.org/10.1007/BF02294547
Wasserman S. (1994). Social network analysis: Methods and applications. Cambridge University Press.
Wise, A. F., Cui, Y., & Jin, W. Q. (2017). Honing in on social learning networks in MOOC forums: examining critical network definition decisions. Proceedings of the Seventh International Conference on Learning Analytics And Knowledge (LAK ‘17), 383-392. http://dx.doi.org/10.1145/3027385.3027446
Zhu, M., Bergner, Y., Zhang, Y., Baker, R., Wang, Y., & Paquette, L. (2016). Longitudinal engagement, performance, and social connectivity: a MOOC case study using exponential random graph models. Proceedings of the Sixth International Conference on Learning Analytics and Knowledge (LAK ‘16). 223-230. http://dx.doi.org/10.1145/288385
Share this article:
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.