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

Autoři: Andreas Heusler aff001;  Dominik Molitor aff002;  Martin Spann aff001
Působiště autorů: Ludwig-Maximilians-University of Munich, Institute of Electronic Commerce and Digital Markets, Munich, Germany aff001;  Gabelli School of Business, Fordham University, New York, NY, United States of America aff002
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
doi: 10.1371/journal.pone.0223064


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.

Klíčová slova:

Finance – Human learning – Intelligence – Learning – Motivation – Supervisors – Stock markets – Social theory


1. Daniel J. Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of Interactive Media in Education. 2012;2012(3):18.

2. Pappano L. The year of the MOOC 2012 [cited 2019 04/21]. Available from:

3. Clow D. MOOCs and the funnel of participation. Proceedings of the Third International Conference on Learning Analytics and Knowledge; 2013: ACM.

4. Kop R. The challenges to connectivist learning on open online networks: Learning experiences during a massive open online course. The International Review of Research in Open and Distributed Learning. 2011;12(3):19–38.

5. Siemens G. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology & Distance Learning. 2005;2(1):3–10.

6. Yang D, Sinha T, Adamson D, Rosé CP. Turn on, tune in, drop out: Anticipating student dropouts in massive open online courses. Proceedings of the 2013 NIPS Data-Driven Education Workshop; 2013; Nevada, USA: NIPS.

7. Knox J. Digital culture clash:“Massive” education in the e-learning and digital cultures MOOC. Distance Education. 2014;35(2):164–77.

8. Alavi M, Leidner DE. Knowledge management and knowledge management systems: Conceptual foundations and research issues. MIS Quarterly. 2001;25(1):107–36.

9. Jarvenpaa SL, Majchrzak A. Research commentary—Vigilant interaction in knowledge collaboration: Challenges of online user participation under ambivalence. Information Systems Research. 2010;21(4):773–84.

10. Kankanhalli A, Tan BC, Wei K-K. Contributing knowledge to electronic knowledge repositories: An empirical investigation. MIS Quarterly. 2005;29(1):113–43.

11. Wasko MM, Faraj S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly. 2005;29(1):35–57.

12. Dickey MD. Engaging by design: How engagement strategies in popular computer and video games can inform instructional design. Educational Technology Research and Development. 2005;53(2):67–83.

13. Gutierrez FJ, Ochoa SF, Zurita G, Baloian N. Understanding student participation in undergraduate course communities: A case study. Information Systems Frontiers. 2016;18(1):7–21.

14. Liu D, Li X, Santhanam R. Digital games and beyond: What happens when players compete? MIS Quarterly. 2013;37(1):111–24.

15. Looyestyn J, Kernot J, Boshoff K, Ryan J, Edney S, Maher C. Does gamification increase engagement with online programs? A systematic review. PLoS ONE 2017;12(3): e0173403. doi: 10.1371/journal.pone.0173403 28362821

16. Fu F-L, Wu Y-L, Ho H-C. An investigation of coopetitive pedagogic design for knowledge creation in Web-based learning. Computers & Education. 2009;53(3):550–62.

17. Afuah A, Tucci CL. Crowdsourcing as a solution to distant search. Academy of Management Review. 2012;37(3):355–75.

18. Tsai W. Social structure of "coopetition" within a multiunit organization: Coordination, competition, and intraorganizational knowledge sharing. Organization Science. 2002;13(2):179–90.

19. Wang M. Integrating organizational, social, and individual perspectives in Web 2.0-based workplace e-learning. Information Systems Frontiers. 2011;13(2):191–205.

20. Mackness J, Mak S, Williams R. The ideals and reality of participating in a MOOC. Proceedings of the 7th International Conference on Networked Learning; 2010; Lancaster, UK.

21. Saadatdoost R, Sim ATH, Jafarkarimi H, Mei Hee J. Exploring MOOC from education and Information Systems perspectives: A short literature review. Educational Review. 2015;67(4):505–18.

22. Fini A. The technological dimension of a massive open online course: The case of the CCK08 course tools. International Review of Research in Open and Distributed Learning. 2009;10(5):74–99.

23. Rodriguez CO. MOOCs and the AI-Stanford Like Courses: Two Successful and Distinct Course Formats for Massive Open Online Courses. European Journal of Open, Distance and E-Learning. 2012;14(2):202–27.

24. Fox A, Patterson D. Crossing the software education chasm. Communications of the ACM. 2012;55(5):44–9.

25. Adamopoulos P. What makes a great MOOC? An interdisciplinary analysis of student retention in online courses. Proceedings of the 34th International Conference on Information Systems; 2013; Milano, Italy: AIS.

26. Jordan K. Initial trends in enrolment and completion of massive open online courses. International Review of Research in Open and Distributed Learning. 2014;15(1):133–60.

27. Lee Y, Choi J. A review of online course dropout research: Implications for practice and future research. Educational Technology Research and Development. 2011;59(5):593–618.

28. Kizilcec RF, Piech C, Schneider E. Deconstructing disengagement: analyzing learner subpopulations in massive open online courses. Proceedings of the 3rd International Conference on Learning Analytics and Knowledge; 2013; Leuven, Belgium: ACM.

29. Onah DF, Sinclair J, Boyatt R. Dropout rates of massive open online courses: behavioural patterns. Proceedings of the 6th International Conference on Education and New Learning Technologies; 2014; Barcelona, Spain: Education and Development

30. Coetzee D, Fox A, Hearst MA, Hartmann B. Should your MOOC forum use a reputation system? Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work and Social Computing; 2014; Baltimore, MD, USA: ACM.

31. Spann M, Skiera B. Internet-based virtual stock markets for business forecasting. Management Science. 2003;49(10):1310–26.

32. Fama EF. Efficient capital markets: II. Journal of Finance. 1991;46(5):1575–617.

33. Hayek FA. The use of knowledge in society. American Economic Review. 1945;35(4):519–30.

34. Smith VL. Markets as economizers of information: Experimental examination of the “Hayek hypothesis”. Economic Inquiry. 1982;20(2):165–79.

35. Chen L, Goes P, Marsden JR, Zhang Z. Design and use of preference markets for evaluation of early stage technologies. Journal of Management Information Systems. 2009;26(3):45–70.

36. Servan‐Schreiber E, Wolfers J, Pennock DM, Galebach B. Prediction markets: Does money matter? Electronic Markets. 2004;14(3):243–51.

37. Dahan E, Soukhoroukova A, Spann M. New product development 2.0: Preference markets—How scalable securities markets identify winning product concepts and attributes. Journal of Product Innovation Management. 2010;27(7):937–54.

38. Graefe A. Prediction markets—Defining events and motivating participation. Foresight-The International Journal of Applied Forecasting. 2008;9(2):30–2.

39. Forsythe R, Nelson F, Neumann GR, Wright J. Anatomy of an experimental political stock market. American Economic Review. 1992;82(5):1142–61.

40. Ostrover S. Employing information markets to achieve truly collaborative sales forecasting. Journal of Business Forecasting. 2005;24(1):9–12.

41. Spann M, Skiera B. Sports Forecasting: A Comparison of the Forecast Accuracy of Prediction Markets, Betting Odds and Tipsters. Journal of Forecasting. 2009;28(1):55–72.

42. Almenberg J, Kittlitz K, Pfeiffer T. An experiment on prediction markets in science. PLoS One. 2009;4(12):e8500. doi: 10.1371/journal.pone.0008500 20041139

43. Soukhoroukova A, Spann M. Sourcing, filtering, and evaluating new product ideas: An empirical exploration of the performance of idea markets. Journal of Product Innovation Management 2012;29(1):100–12.

44. Heusler A, Spann M. Knowledge stock exchanges: A co-opetitive crowdsourcing mechanism for e-learning. Proceedings of the European Conference on Information Systems; 2014; Tel-Aviv, Israel: AIS.

45. Poetz MK, Schreier M. The value of crowdsourcing: can users really compete with professionals in generating new product ideas? Journal of Product Innovation Management. 2012;29(2):245–56.

46. Deci EL, Ryan RM. Handbook of self-determination research: University Rochester Press; 2004.

47. Festinger L. A theory of social comparison processes. Human Relations. 1954;7(2):117–40.

48. Bower J, Bunn D. Experimental analysis of the efficiency of uniform-price versus discriminatory auctions in the England and Wales electricity market. Journal of Economic Dynamics and Control. 2001;25(3–4):561–92.

49. Hanson R. Combinatorial information market design. Information Systems Frontiers. 2003;5(1):107–19.

50. Othman A, Pennock DM, Reeves DM, Sandholm T. A practical liquidity-sensitive automated market maker. Proceedings of the 11th ACM Conference on Electronic Commerce; 2010; Cambridge, MA, USA: ACM.

51. Slamka C, Skiera B, Spann M. Prediction market performance and market liquidity: A comparison of automated market makers. IEEE Transactions on Engineering Management. 2013;60(1):169–85.

52. Berg JE, Rietz TA. Prediction markets as decision support systems. Information Systems Frontiers. 2003;5(1):79–93.

53. The Augmented Trader. MOOC Student Demographics 2013 [cited 2019 07/02]. Available from:

54. Emanuel EJ. Online education: MOOCs taken by educated few. Nature. 2013;503(7476):342.

55. Shaver JM. Accounting for endogeneity when assessing strategy performance: does entry mode choice affect FDI survival? Management Science. 1998;44(4):571–85.

56. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika. 1983;70(1):41–55.

57. Heckman JJ, Ichimura H, Todd P. Matching as an econometric evaluation estimator. Review of Economic Studies. 1998;65(2):261–94.

58. Dehejia RH, Wahba S. Propensity score-matching methods for nonexperimental causal studies. Review of Economics and Statistics. 2002;84(1):151–61.

59. Mithas S, Krishnan MS. From association to causation via a potential outcomes approach. Information Systems Research. 2009;20(2):295–313.

60. Imbens GW. Nonparametric estimation of average treatment effects under exogeneity: A review. Review of Economics and Statistics. 2004;86(1):4–29.

61. Morita JG, Lee TW, Mowday RT. The regression-analog to survival analysis: A selected application to turnover research. Academy of Management Journal. 1993;36(6):1430–64.

62. Singer JD, Willett JB. It’s about time: Using discrete-time survival analysis to study duration and the timing of events. Journal of Educational Statistics. 1993;18(2):155–95.

63. Wager S, Athey S. Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association. 2018;113(523):1228–42.

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