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Examining MOOC superposter behavior using social network analysis

Published:15 January 2020Publication History

ABSTRACT

This paper examines quantity and quality superposter value creation within Coursera Massive Open Online Courses (MOOC) forums using a social network analysis (SNA) approach. The value of quantity superposters (i.e. students who post significantly more often than the majority of students) and quality superposters (i.e. students who receive significantly more upvotes than the majority of students) is assessed using Stochastic Actor-Oriented Modeling (SAOM) and network centrality calculations. Overall, quantity and quality superposting was found to have a significant effect on tie formation within the discussion networks. In addition, quantity and quality superposters were found to have higher-than-average information brokerage capital within their networks.

References

  1. J. Dijsselbloem, "The Rise of MOOCs: Can Online Distance Learning Replace Traditional Education", Diggit Magazine, November 2018.Google ScholarGoogle Scholar
  2. J. Huang, A. Dasgupta, A. Ghost, J. Manning, and M. Sanders, "Superposter behavior in MOOC forums," Proceedings of the First ACM Conference on Learning at Scale Conference, pp. 117--126, March 2014.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. L. Rossi and O. Gnawali, "Language independent analysis and classification of discussion threads in Coursera MOOC forums," IEEE International Conference on Information Reuse and Integration (IRI), August 2014.Google ScholarGoogle Scholar
  4. T. Sinha, "Supporting MOOC instruction with social network analysis," ACM, January 2014.Google ScholarGoogle Scholar
  5. R. Acton, L. Jasny, "An Introduction to Network Analysis with R and statnet," Subbelt XXXIV Workshop Series, February 2014.Google ScholarGoogle Scholar
  6. "Stochastic actor-oriented modeling for studying homophily and social influence in OSS projects," Empirical Software Engineering an International Journal, vol. 22, February 2017.Google ScholarGoogle Scholar
  7. R. Ripley, R. Snijders, Z. Boda, A. Voros, P. Preciado, Manual for RSiena. University of Oxford, April 2019.Google ScholarGoogle Scholar
  8. I. McCulloh, H. Armstrong, and A. Johnson, Social Network Analysis with Applications. Wiley, July 2013.Google ScholarGoogle Scholar
  9. "Betweenness Centrality (Centrality Measure)," GeeksforGeeks: A computer science portal for geeks.Google ScholarGoogle Scholar
  10. M. Saqr, U. Fors, M. Tedre, and J. Nouri, "How social network analysis can be used to monitor online collaborative learning and guide an informed intervention," PLoS One, vol. 13(3), March 2018Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    ASONAM '19: Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
    August 2019
    1228 pages
    ISBN:9781450368681
    DOI:10.1145/3341161

    Copyright © 2019 ACM

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    New York, NY, United States

    Publication History

    • Published: 15 January 2020

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    ASONAM '19 Paper Acceptance Rate41of286submissions,14%Overall Acceptance Rate116of549submissions,21%

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