Positive Impact of Collaborative Chat Participation in an edX MOOC

  • Oliver Ferschke
  • Diyi Yang
  • Gaurav Tomar
  • Carolyn Penstein Rosé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9112)


A major limitation of the current generation of MOOCs is a lack of opportunity for students to make use of each other as resources. Analyses of attrition and learning in MOOCs both point to the importance of social engagement for motivational support and overcoming difficulties with material and course procedures. In this paper we evaluate an intervention that makes synchronous collaboration opportunities available to students in an edX MOOC. We have implemented a Lobby program that students can access via a live link at any time. Upon entering the Lobby, they are matched with other students that are logged in to it. Once matched, they are provided with a link to a chat room where they can work with their partner students on a synchronous collaboration activity, supported by a conversational computer agent. Results of a survival model in which we control for level of effort suggest that having experienced a collaborative chat is associated with a slow down in the rate of attrition over time by a factor of two. We discuss implications for design, limitations of the current study, and directions for future research.


Collaborative reflection Survival analysis Massive open online courses 


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  1. 1.
    Adamson, D., Dyke, G., Jang, H.J., Rosé, C.P.: Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs. International Journal of AI in Education 24(1), 91–121 (2014)Google Scholar
  2. 2.
    Ai, H., Kumar, R., Nguyen, D., Nagasunder, A., Rosé, C.P.: Exploring the effectiveness of social capabilities and goal alignment in computer supported collaborative learning. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part II. LNCS, vol. 6095, pp. 134–143. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  3. 3.
    Chaudhuri, S., Kumar, R., Joshi, M., Terrell, E., Higgs, F., Aleven, V., Penstein Rosé, C.: It’s not easy being green: supporting collaborative ``green design’’ learning. In: Woolf, B.P., A\”ımeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 807–809. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Chaudhuri, S., Kumar, R., Howley, I., Rosé, C.P.: Engaging collaborative learners with helping agents. In: Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modeling, pp. 365-372. IOS Press (2009)Google Scholar
  5. 5.
    Clarke, S., Chen, G., Stainton, K., Katz, S., Greeno, J., Resnick, L., Howley, H., Adamson, D., Rosé, C.P.: The impact of CSCL beyond the online environment. In: Proceedings of Computer Supported Collaborative Learning (2013)Google Scholar
  6. 6.
    Dillenbourg, P.: Over-scripting CSCL: The risks of blending collaborative learning with instructional design. In: Three worlds of CSCL - Can we support CSCL? pp. 61–91 (2002)Google Scholar
  7. 7.
    Erkens, G., Janssen, J.: Automatic Coding of Dialogue Acts in Collaboration Protocols. International Journal of Computer Supported Collaborative Learning 3, 447–470 (2008)CrossRefGoogle Scholar
  8. 8.
    Kirschner, F., Paas, F., Kirschner, P.A.: A cognitive load approach to collaborative learning: United brains for complex tasks. Educational Psychology Review 21, 31–42 (2009)CrossRefGoogle Scholar
  9. 9.
    Kobbe, L., Weinberger, A., Dillenbourg, P., Harrer, A., Hämäläinen, R., Häkkinen, P., Fischer, F.: Specifying computer-supported collaboration scripts. International Journal of Computer-Supported Collaborative Learning 2(2), 211–224 (2007)CrossRefGoogle Scholar
  10. 10.
    Kollar, I., Fischer, F., Hesse, F.W.: Collaborative scripts - a conceptual analysis. Educational Psychology Review 18(2), 159–185 (2006)CrossRefGoogle Scholar
  11. 11.
    Kumar, R., Rosé, C.P., Wang, Y.C., Joshi, M., Robinson, A.: Tutorial dialogue as adaptive collaborative learning support. In: Proceedings of Artificial Intelligence in Education (2007)Google Scholar
  12. 12.
    Kumar, R., Ai, H., Beuth, J.L., Rosé, C.P.: Socially Capable Conversational Tutors Can Be Effective in Collaborative Learning Situations. In: Aleven, V., Kay, J., Mostow, J. (eds.) ITS 2010, Part I. LNCS, vol. 6094, pp. 156–164. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  13. 13.
    Kumar, R., Rosé, C.P.: Architecture for Building Conversational Agents that Support Collaborative Learning. IEEE Transactions on Learning Technologies 4(1) (2011)Google Scholar
  14. 14.
    McLaren, B., Scheuer, O., De Laat, M., Hever, R., de Groot, R., Rosé, C.P.: Using machine learning techniques to analyze and support mediation of student e-discussions. In: Proceedings of Artificial Intelligence in Education, pp. 331–338. IOS Press (2007)Google Scholar
  15. 15.
    Mu, J., Stegmann, K., Mayfield, E., Rosé, C.P., Fischer, F.: The ACODEA Framework: Developing Segmentation and Classification Schemes for Fully Automatic Analysis of Online Discussions. International Journal of Computer Supported Collaborative Learning 7(2), 285–305 (2012)CrossRefGoogle Scholar
  16. 16.
    Rosé C.P., VanLehn, K.: An Evaluation of a Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals. International Journal of AI in Education 15(4) (2005)Google Scholar
  17. 17.
    Rosé, C.P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F.: Analyzing collaborative learning processes automatically: Exploiting the advances of computational linguistics in computer-supported collaborative learning. The International Journal of Computer-Supported Collaborative Learning 3(3), 237–271 (2008)CrossRefGoogle Scholar
  18. 18.
    Soller, A., Lesgold, A.: Modeling the process of collaborative learning. In: Proceedings of the International Workshop on New Technologies in Collaborative Learning. Japan: Awaiji–Yumebutai (2000)Google Scholar
  19. 19.
    Webb, N.M., Palinscar, A.S.: Group processes in the classroom. In: Berliner, D.C., Calfee, R.C. (eds.) Handbook of educational psychology, pp. 841–873. Prentice Hall, New York (1996)Google Scholar
  20. 20.
    Wecker, C., Fischer, F.: Fading scripts in computer-supported collaborative learning: the role of distributed monitoring. In: CSCL 2007 Proceedings of the 8th International Conference on Computer Supported Collaborative Learning, pp. 764–772 (2007)Google Scholar
  21. 21.
    Weinberger, A., Stegmann, K., Fischer, F., Mandl, H.: Scripting argumentative knowledge construction in computer-supported learning environments. In: Scripting Computer-Supported Collaborative Learning, CSCL Book Series vol. 6, ch. 6, pp. 191–211 (2007)Google Scholar
  22. 22.
    Wen, M., Yang, D., Rosé, D.: Linguistic reflections of student engagement in massive open online courses. In: Proceedings of the International Conference on Weblogs and Social Media (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Oliver Ferschke
    • 1
  • Diyi Yang
    • 1
  • Gaurav Tomar
    • 1
  • Carolyn Penstein Rosé
    • 1
  1. 1.Carnegie Mellon UniversityPittsburghUSA

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