Journal of the Knowledge Economy

, Volume 7, Issue 2, pp 461–487 | Cite as

How MOOCs Link with Social Media

  • Dimitrios Kravvaris
  • Katia Lida Kermanidis
  • Georgios Ntanis


The purpose of this paper is to present a survey conducted on massively open online courses (MOOCs) from Coursera and how they are linked with social media. It examines the web data that have been retrieved from Coursera’s MOOCs information pages that can be recommended by the users of the social networks and, in turn, be shared by them. What should be stressed, however, is that our focus is on the study of those data that are open and accessible to everyone and not only to registered users of MOOCs. The aim of our study, therefore, is to find out the attributes of these information pages that can characterize a course popular and those that are considered to be the most important for the users’ recommendation procedure. It is shown that the courses providing information about the assignments and the exams of the course are mostly recommended in the social media. Furthermore, we proved the correlation among the three largest social networks: Facebook, Google+, and Twitter, based on the information pages’ data, using statistical and machine learning methods. Finally, statistical experiments were carried out concerning the MOOCs users’ shares to social media.


MOOC Social media Clustering Attribute selection Correlation Regression 


  1. Aguillo, I. (2010). Web, webometrics and the ranking of universities. In Proceedings of the 3rd European Network of Indicators Designers Conference on STI Indicators for Policymaking and strategic decision, CNAM, Paris (to appear, 2010).Google Scholar
  2. American Council on Education. (2014). College credit recommendation service. Accessed 21 Jan 2014.
  3. Anderson, N. (2012). Elite education for the masses. Washington Post. http://www.washington Accessed 20 Jan 2014.
  4. Bersin, J. (2013). The MOOC marketplace takes off. 2013/11/30/the-mooc-marketplace-takes-off/. Accessed 20 Jan 2014.
  5. Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).Google Scholar
  6. Cecil-Reed. P. (2013). The MOOC takeover Pt 1: founders and innovators. educational-trends/ocw/moocs-founders-and-innovators.html. Accessed 20 Jan 2014.
  7. Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5), 318–331.CrossRefGoogle Scholar
  8. Collins, K. (2013). Facebook could become a distribution vehicle for MOOCs, says global policy chief. Accessed 20 Jan 2014.
  9. Coursera. (2013a). A triple milestone: 107 partners, 532 courses, 5.2 million students. Accessed 20 Jan 2014.
  10. Coursera. (2013b). Introducing signature track. Accessed 20 Jan 2014.
  11. Coursera. (2014). Company’s website. Accessed 20 Jan 2014.
  12. de Waard, I., Koutropoulos, A., Keskin, N., Abajian, S. C., Hogue, R., Rodriguez, C. O., et al. (2011). Exploring the MOOC format as a pedagogical approach for mLearning. Beijing: Proceedings from mLearn 2011.Google Scholar
  13. DeBoer, J., Stump, G. S., Seaton, D., & Breslow, L. (2013). Diversity in MOOC students’ backgrounds and behaviors in relationship to performance in 6.002 x. In Proceedings of the Sixth Learning International Networks Consortium Conference.Google Scholar
  14. Delfino, M., & Persico, D. (2007). Online or face–to–face? Experimenting with different techniques in teacher training. Journal of Computer Assisted Learning, 23(5), 351–365.CrossRefGoogle Scholar
  15. Dellarocas, C., & Van Alstyne, M. (2013). Money models for MOOCs. Communications of the ACM, 56(8), 25–28. doi: 10.1145/2492007.2492017.CrossRefGoogle Scholar
  16. Domingos, P. (2005). Mining social networks for viral marketing. IEEE Intelligent Systems, 20(1), 80–82.CrossRefGoogle Scholar
  17. Färber, I., Günnemann, S., Kriegel, H. P., Kröger, P., Müller, E., Schubert, E., et al. (2010). On using class-labels in evaluation of clusterings. In MultiClust: 1st International Workshop on Discovering, Summarizing and Using Multiple Clusterings Held in Conjunction with KDD.Google Scholar
  18. Frankola, K. (2001). Why online learners drop out. WORKFORCE-COSTA MESA, 80(10), 52–61.Google Scholar
  19. Grünewald, F., Meinel, C., Totschnig, M., & Willems, C. (2013). Designing MOOCs for the support of multiple learning styles. In Scaling up Learning for Sustained Impact (pp. 371-382). Springer Berlin Heidelberg.Google Scholar
  20. Gundecha, P., & Liu, H. (2012). Mining social media: a brief introduction. Tutorials in Operations Research, 1(4).Google Scholar
  21. Hall, M. A., & Holmes, G. (2003). Benchmarking attribute selection techniques for discrete class data mining. Knowledge and Data Engineering, IEEE Transactions on, 15(6), 1437–1447.CrossRefGoogle Scholar
  22. Han, J., Kamber, M., & Pei, J. (2006). Data mining: concepts and techniques. Morgan Kaufmann.Google Scholar
  23. Hearst, M. A., Dumais, S. T., Osman, E., Platt, J., & Scholkopf, B. (1998). Support vector machines, intelligent systems and their applications. IEEE, 13(4), 18–28.Google Scholar
  24. Huang, J., Dasgupta, A., Ghosh, A., Manning, J., & Sanders, M. (2014). Superposter behavior in MOOC forums.Google Scholar
  25. Indra Devi, M., Rajaram, R., & Selvakuberan, K. (2008). Generating best features for web page classification. Webology, 5(1), 52.Google Scholar
  26. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C., Silverman, R., & Wu, A. Y. (2000). The analysis of a simple k-means clustering algorithm. In Proceedings of the sixteenth annual symposium on computational geometry, (pp. 100-109).Google Scholar
  27. Kirkby, R., Frank, E., & Reutemann, P. (2006). WEKA explore user guide. Retrieved from Accessed 20 Jan 2014.
  28. Koutropoulos, A., Gallagher, M., Abajian, S., de Waard, I., Hogue, R., Keskin, N., & Rodriguez, C. (2012). Emotive vocabulary in MOOCs: context and participant retention. The European Journal of Open, Distance and E-Learning, vol. 1.Google Scholar
  29. Lennon, C., & Burdick, H. (2004). The Lexile framework as an approach for reading measurement and success. Accessed 20 Jan 2014.
  30. Liaw, A., &Wiener, M. (2002). Classification and regression by randomForest. R News, 2/3:18–22. ISNN 1609-3631.Google Scholar
  31. Madria, S. K., Bhowmick, S. S., Ng, W. K., & Lim, E. P. (1999). Research issues in web data mining. In data warehousing and knowledge discovery (pp. 303-312). Springer Berlin Heidelberg.Google Scholar
  32. Mak, S., Williams, R., & Mackness, J. (2010). Blogs and forums as communication and learning tools in a MOOC. In: Proceedings of the 7th International Conference on Networked Learning. pp. 275-285. ISBN 9781862202252.Google Scholar
  33. McAndrew, P. (2013). Learning from open design: running a learning design MOOC. eLearning Papers, (33).Google Scholar
  34. Montgomery, G. C., Peck, E., &Vining, G. G. (2010). Introduction to linear regression analysis. Published by John Wiley and Sons, Inc. ISBN: 978-0-470-54281-1.Google Scholar
  35. MOOCs Mentor. (2013). MOOCs mentor releases dedicated MOOCs helpline, creates revolution. Accessed 20 Jan 2014.
  36. Murray, M., Pérez, J., Geist, D., & Hedrick, A. (2012). Student interaction with online course content: build it and they might come. Journal of Information Technology Education: Research, 11(1), 125–140.Google Scholar
  37. Norusis, M. (2008). SPSS 16.0 Guide to Data Analysis. NJ: Published by Prentice Hall Press Upper Saddle River. ISBN: 0136061362 9780136061366.Google Scholar
  38. Piech, C., Huang, J., Chen, Z., Do, C., Ng, A., & Koller, D. (2013). Tuned models of peer assessment in MOOCs. arXiv preprint arXiv:1307.2579.Google Scholar
  39. Pitt, E., & Nayak, R. (2007). The use of various data mining and feature selection methods in the analysis of a population survey dataset. AIDM ‘07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining, 84, 83–93.Google Scholar
  40. Rafter, M.V. (2013). MOOCs could lower cost of college. Accessed 20 Jan 2014.
  41. Rayyan, S., Seaton, D. T., Belcher, J., Pritchard, D. E., & Chuang, I. (2013). Participation and performance In 8.02 x Electricity And Magnetism: The First Physics MOOC From MITx. arXiv preprint arXiv:1310.3173.Google Scholar
  42. Robnik-Šikonja, M. (2004). Improving random forests. In Machine Learning: ECML 2004, pp. 359-370. Springer Berlin Heidelberg.Google Scholar
  43. Rodriguez, O. (2012). MOOCs and the AI-Stanford like courses: two successful and distinct course formats for massive open online courses. European Journal of Open and Distance Learning. Accessed 20 Jan 2014.
  44. Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. (2011). Handbook of educational data mining. Published by CRC Press, 2011.Google Scholar
  45. Shah, D. (2013). MOOCs in 2013: breaking down the numbers. Accessed 21 Jan 2014.
  46. Shortell, T. (2001). Online textbook: an introduction to data analysis & presentation, Chapter 18: Correlations. Accessed 20 Jan 2014.
  47. Srivastava, J., Cooley, R., Deshpande, M. & Tan, P. N., (2000). Web usage mining: discovery and applications of usage patterns from Web data, ACM SIGKDD Explorations Newsletter, v.1 n.2. doi: 10.1145/846183.846188.
  48. Thakur, M. (2007). The impact of ranking systems on higher education and its stakeholders. Journal of Institutional Research, 13(1), 83–96.Google Scholar
  49. Veeramachaneni, K., Dernoncourt, F., Taylor, C., Pardos, Z., & O’Reilly, U. M. (2013). Moocdb: Developing data standards for MOOC data science. In AIED 2013 Workshops Proceedings Volume (p. 17).Google Scholar
  50. Vora, P., & Oza, B. (2013). A survey on K-mean clustering and particle swarm optimization. International Journal of Science and Modern Engineering (IJISME), 1, 24–26.Google Scholar
  51. Vrasidas, C., & McIsaac, M. S. (1999). Factors influencing interaction in an online course. American Journal of Distance Education, 13(3), 22–36.CrossRefGoogle Scholar
  52. Waldrop, M. M. (2013). Massive open online courses are transforming higher education—and providing fodder for scientific research. Nature, 495, 160–163.CrossRefGoogle Scholar
  53. Weka. (2013). Weka 3: Data Mining Software in Java. University of Waikato. Accessed 20 Jan 2014.

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Dimitrios Kravvaris
    • 1
  • Katia Lida Kermanidis
    • 1
  • Georgios Ntanis
    • 1
  1. 1.Department of InformaticsIonian UniversityCorfuGreece

Personalised recommendations