Analyzing Interactivity in Asynchronous Video Discussions

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8523)


Evaluating online discussions is a complex task for educators. Information systems may support instructors and course designers to assess the quality of an asynchronous online discussion tool. Interactivity on a human-to-human, human-to-computer or human-to-content level are focal elements of such quality assessment. Nevertheless existing indicators used to measure interactivity oftentimes rely on manual data collection. One major contribution of this paper is an updated overview about indicators which are ready for automatic data collection and processing. Following a design science research approach we introduce measures for a consumer side of interactivity and contrast them with a producer’s perspective. For this purpose we contrast two ratio measures ‘viewed posts prior to a statement’ and ‘viewed posts after a statement’ created by a student. In order to evaluate these indicators, we apply them to Pinio, an innovative asynchronous video discussion tool, used in a virtual seminar.


Online discussion asynchronous video discussion educational data mining interactivity higher education 


  1. 1.
    Bures, E.M., Abrami, P.C., Amundsen, C.: Student motivation to learn via computer conferencing. Research in higher Education 41(5), 593–621 (2000)CrossRefGoogle Scholar
  2. 2.
    Swan, K., Shea, P.: The development of virtual learning communities. In: Learning Together Online: Research on Asynchronous Learning Networks, pp. 239–260 (2005)Google Scholar
  3. 3.
    Weber, P., Rothe, H.: Social networking services in e-learning. In: Bastiaens, T., Marks, G. (eds.) Education and Information Technology 2013: A Selection of AACE Award Papers, AACE, vol. 1, pp. 89–99. Chesapeake (2013)Google Scholar
  4. 4.
    Kear, K.: Peer learning using asynchronous discussion systems in distance education. Open Learning: The Journal of Open, Distance and e-Learning 19(2), 151–164 (2004)CrossRefGoogle Scholar
  5. 5.
    Hammond, M.: A review of recent papers on online discussion in teaching and learning in higher education. Journal of Asynchronous Learning Networks 9(3), 9–23 (2005)MathSciNetGoogle Scholar
  6. 6.
    Cheng, C.K., Paré, D.E., Collimore, L., et al.: Assessing the effectiveness of a voluntary online discussion forum on improving students’ course performance. Computers & Education 56(1), 253–261 (2011)CrossRefGoogle Scholar
  7. 7.
    Webb, E., Jones, A., Barker, P., et al.: Using e-learning dialogues in higher education. Innovations in Education and Teaching International 41(1), 93–103 (2004)CrossRefGoogle Scholar
  8. 8.
    Jyothi, S., McAvinia, C., Keating, J.: A visualisation tool to aid exploration of students’ interactions in asynchronous online communication. Computers & Education 58(1), 30–42 (2012)CrossRefGoogle Scholar
  9. 9.
    Wise, A.F., Perera, N., Hsiao, Y., et al.: Microanalytic case studies of individual participation patterns in an asynchronous online discussion in an undergraduate blended course. The Internet and Higher Education 15(2), 108–117 (2012)CrossRefGoogle Scholar
  10. 10.
    Beaudoin, M.F.: Learning or lurking?: Tracking the “invisible” online student. The Internet and Higher Education 5(2), 147–155 (2002)CrossRefGoogle Scholar
  11. 11.
    Tobarra, L., Robles-Gómez, A., Ros, S., et al.: Analyzing the students’ behavior and relevant topics in virtual learning communities. Computers in Human Behavior 31, 659–669 (2014)CrossRefGoogle Scholar
  12. 12.
    Dringus, L.P., Ellis, T.: Using data mining as a strategy for assessing asynchronous discussion forums. Computers & Education 45(1), 141–160 (2005)CrossRefGoogle Scholar
  13. 13.
    Peffers, K., Tuunanen, T., Rothenberger, M.A., et al.: A design science research methodology for information systems research. Journal of management information systems 24(3), 45–77 (2007)CrossRefGoogle Scholar
  14. 14.
    Harasim, L.: Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education 3(1), 41–61 (2000)CrossRefGoogle Scholar
  15. 15.
    Kaye, A.: Computer-mediated communication and distance education. In: Mason, R., Kaye, A. (eds.) Mindweave: Communication, Computers, and Distance Education. Pergamon, New York (1989)Google Scholar
  16. 16.
    Gibbs, W., Simpson, L.D., Bernas, R.S.: An analysis of temporal norms in online discussions. International Journal of Instructional Media 35(1), 63 (2008)Google Scholar
  17. 17.
    Woo, Y., Reeves, T.C.: Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education 10(1), 15–25 (2007)CrossRefGoogle Scholar
  18. 18.
    Jonassen, D.H., Kwon II, H.: Communication patterns in computer mediated versus face-to-face group problem solving. Educational Technology Research and Development 49(1), 35–51 (2001)CrossRefGoogle Scholar
  19. 19.
    Prestera, G.E., Moller, L.A.: Exploiting opportunities for knowledge-building in asynchronous distance learning environments. Quarterly Review of Distance Education 2(2), 93–104 (2001)Google Scholar
  20. 20.
    Peters, V.L., Hewitt, J.: An investigation of student practices in asynchronous computer conferencing courses. Computers & Education 54(4), 951–961 (2010)CrossRefGoogle Scholar
  21. 21.
    Johnson, L., Adams Becker, S., Cummins, M., Estrada, V., Freeman, A., Ludgate, H.: NMC Horizon Report: 2013 Higher Education Edition (2013), (accessed February 15, 2013)
  22. 22.
    Campbell, J.P., DeBlois, P.B., Oblinger, D.G.: Academic Analytics. Educause Review 42(4), 40–57 (2007)Google Scholar
  23. 23.
    Elias, T.: Learning Analytics: Definitions, Processes and Potential (2011),
  24. 24.
    Siemens, G., Long, P.: Penetrating the fog: Analytics in learning and education. Educause Review 46(5), 30–32 (2011)Google Scholar
  25. 25.
    Romero, C., Ventura, S.: Educational data mining: A survey from 1995 to 2005. Expert Systems with Applications 33(1), 135–146 (2007)CrossRefGoogle Scholar
  26. 26.
    Romero-Zaldivar, V., Pardo, A., Burgos, D., et al.: Monitoring student progress using virtual appliances: A case study. Computers & Education 58(4), 1058–1067 (2012)CrossRefGoogle Scholar
  27. 27.
    Bernard, R.M., Abrami, P.C., Borokhovski, E., et al.: A meta-analysis of three types of interaction treatments in distance education. Review of Educational Research 79(3), 1243–1289 (2009)CrossRefGoogle Scholar
  28. 28.
    Moore, M.G.: Editorial: Three types of interaction. American Journal of Distance Education 3(2), 86–89 (1989)CrossRefGoogle Scholar
  29. 29.
    Hamuy, E., Galaz, M.: Information versus communication in course management system participation. Computers & Education 54(1), 169–177 (2010)CrossRefGoogle Scholar
  30. 30.
    Mazzolini, M., Maddison, S.: When to jump in: The role of the instructor in online discussion forums. Computers & Education 49(2), 193–213 (2007)CrossRefGoogle Scholar
  31. 31.
    Thomas, M.J.W.: Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning 18(3), 351–366 (2002)CrossRefGoogle Scholar
  32. 32.
    Bayer, J., Bydzovská, H., Géryk, J., Obšıvac, T., Popelınský, L.: Predicting drop-out from social behaviour of students. In: Proceedings of the 5th International Conference on Educational Data Mining (2012)Google Scholar
  33. 33.
    Romero, C., López, M., Luna, J., et al.: Predicting students’ final performance from participation in on-line discussion forums. Computers & Education 68(0), 458–472 (2013), doi:10.1016/j.compedu.2013.06.009CrossRefGoogle Scholar
  34. 34.
    Thomas, M.J.W.: Learning within incoherent structures: The space of online discussion forums. Journal of Computer Assisted Learning 18(3), 351–366 (2002)CrossRefGoogle Scholar
  35. 35.
    Hung, J., Zhang, K.: Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching (2008)Google Scholar
  36. 36.
    Kumar, V., Chadha, A.: An Empirical Study of the Applications of Data Mining Techniques in Higher Education. International Journal of Advanced Computer Science and Applications 2(3), 80–84 (2011)CrossRefGoogle Scholar
  37. 37.
    Lin, F., Hsieh, L., Chuang, F.: Discovering genres of online discussion threads via text mining. Computers & Education 52(2), 481–495 (2009)CrossRefGoogle Scholar
  38. 38.
    Ebner, M., Holzinger, A., Catarci, T.: Lurking: An underestimated human-computer phenomenon. IEEE Multimedia 12(4), 70–75 (2005)CrossRefGoogle Scholar
  39. 39.
    Lehr, C.: Web 2.0 in der universitären Lehre (2011), (received at February 2, 2014)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department Business Information SystemsFreie Universität BerlinGermany

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