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How Knowledge Stock Exchanges can increase student success in Massive Open Online Courses

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  • Andreas Heusler
  • Dominik Molitor
  • Martin Spann

Abstract

Massive Open Online Courses (MOOCs) allow lecturers to overcome spatiotemporal boundaries and reach large numbers of participants. However, the completion rates of MOOCs are relatively low, a critical obstacle to their ultimate success. Existing literature suggests that strengthening student interaction has the potential to increase student commitment. The goal of this study is to develop a novel, market-based knowledge-sharing method that fosters student engagement and interaction in MOOCs, addressing the problem of low completion rates and demonstrating how MOOC engagement can lead to greater student success. The proposed method, “Knowledge Stock Exchange” (KSX), is derived from the concept of crowd-based intelligence mechanisms for incentive-compatible information aggregation. Using a popular MOOC as the focus of our empirical study, we show that the KSX method increases student interaction as well as MOOC completion rates. Moreover, we find that KSX participation has a significant positive effect on participants’ exam grades.

Suggested Citation

  • Andreas Heusler & Dominik Molitor & Martin Spann, 2019. "How Knowledge Stock Exchanges can increase student success in Massive Open Online Courses," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-17, September.
    • Handle: RePEc:plo:pone00:0223064
    DOI: 10.1371/journal.pone.0223064
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    References listed on IDEAS

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    1. Martin Spann & Bernd Skiera, 2003. "Internet-Based Virtual Stock Markets for Business Forecasting," Management Science, INFORMS, vol. 49(10), pages 1310-1326, October.
    2. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    3. Joyce E. Berg & Thomas A. Rietz, 2003. "Prediction Markets as Decision Support Systems," Information Systems Frontiers, Springer, vol. 5(1), pages 79-93, January.
    4. Andreas Graefe, 2008. "Prediction Markets – Defining Events and Motivating Participation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 9, pages 30-32, Spring.
    5. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    6. Ezekiel J. Emanuel, 2013. "MOOCs taken by educated few," Nature, Nature, vol. 503(7476), pages 342-342, November.
    7. J. Myles Shaver, 1998. "Accounting for Endogeneity When Assessing Strategy Performance: Does Entry Mode Choice Affect FDI Survival?," Management Science, INFORMS, vol. 44(4), pages 571-585, April.
    8. Martin Spann & Bernd Skiera, 2009. "Sports forecasting: a comparison of the forecast accuracy of prediction markets, betting odds and tipsters," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(1), pages 55-72.
    9. Sirkka L. Jarvenpaa & Ann Majchrzak, 2010. "Research Commentary ---Vigilant Interaction in Knowledge Collaboration: Challenges of Online User Participation Under Ambivalence," Information Systems Research, INFORMS, vol. 21(4), pages 773-784, December.
    10. Johan Almenberg & Ken Kittlitz & Thomas Pfeiffer, 2009. "An Experiment on Prediction Markets in Science," PLOS ONE, Public Library of Science, vol. 4(12), pages 1-7, December.
    11. Francisco J. Gutierrez & Sergio F. Ochoa & Gustavo Zurita & Nelson Baloian, 2016. "Understanding student participation in undergraduate course communities: A case study," Information Systems Frontiers, Springer, vol. 18(1), pages 7-21, February.
    12. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity Score-Matching Methods For Nonexperimental Causal Studies," The Review of Economics and Statistics, MIT Press, vol. 84(1), pages 151-161, February.
    13. Robin Hanson, 2003. "Combinatorial Information Market Design," Information Systems Frontiers, Springer, vol. 5(1), pages 107-119, January.
    14. Bower, John & Bunn, Derek, 2001. "Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market," Journal of Economic Dynamics and Control, Elsevier, vol. 25(3-4), pages 561-592, March.
    15. Smith, Vernon L, 1982. "Markets as Economizers of Information: Experimental Examination of the "Hayek Hypothesis"," Economic Inquiry, Western Economic Association International, vol. 20(2), pages 165-179, April.
    16. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    17. Forsythe, Robert & Forrest Nelson & George R. Neumann & Jack Wright, 1992. "Anatomy of an Experimental Political Stock Market," American Economic Review, American Economic Association, vol. 82(5), pages 1142-1161, December.
    18. Sunil Mithas & M. S. Krishnan, 2009. "From Association to Causation via a Potential Outcomes Approach," Information Systems Research, INFORMS, vol. 20(2), pages 295-313, June.
    19. James J. Heckman & Hidehiko Ichimura & Petra Todd, 1998. "Matching As An Econometric Evaluation Estimator," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(2), pages 261-294.
    20. Minhong Wang, 2011. "Integrating organizational, social, and individual perspectives in Web 2.0-based workplace e-learning," Information Systems Frontiers, Springer, vol. 13(2), pages 191-205, April.
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