Skip to main content
SpringerLink
Log in

How MOOCs Link with Social Media

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Notes

  1. Information retrieved from Coursera’s blog article “Introducing Signature Track” (Coursera 2013b)

  2. According to American Council on Education (2014)

  3. Information retrieved from Coursera’s blog article “A Triple Milestone” (Coursera 2013a)

References

  • 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).

  • American Council on Education. (2014). College credit recommendation service. http://www2.acenet.edu/credit/?fuseaction=browse.getOrganizationDetail&FICE=1007444. Accessed 21 Jan 2014.

  • Anderson, N. (2012). Elite education for the masses. Washington Post. http://www.washington post.com/local/education/elite-education-for-the-masses/2012/11/03/c2ac8144-121b-11e2-ba83-a7a396e6b2a7_story.html. Accessed 20 Jan 2014.

  • Bersin, J. (2013). The MOOC marketplace takes off. http://www.forbes.com/sites/joshbersin/ 2013/11/30/the-mooc-marketplace-takes-off/. Accessed 20 Jan 2014.

  • Caruana, R., & Niculescu-Mizil, A. (2006). An empirical comparison of supervised learning algorithms. Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).

  • Cecil-Reed. P. (2013). The MOOC takeover Pt 1: founders and innovators. http://www.onlinecolleges.com/ educational-trends/ocw/moocs-founders-and-innovators.html. Accessed 20 Jan 2014.

  • 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.

    Article  Google Scholar 

  • Collins, K. (2013). Facebook could become a distribution vehicle for MOOCs, says global policy chief. http://www.wired.co.uk/news/archive/2013-11/01/facebook-moocs. Accessed 20 Jan 2014.

  • Coursera. (2013a). A triple milestone: 107 partners, 532 courses, 5.2 million students. http://blog.coursera.org/post/64907189712/a-triple-milestone-107-partners-532-courses-5-2. Accessed 20 Jan 2014.

  • Coursera. (2013b). Introducing signature track. http://blog.coursera.org/post/40080531667/signaturetrack. Accessed 20 Jan 2014.

  • Coursera. (2014). Company’s website. https://www.coursera.org. Accessed 20 Jan 2014.

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • Dellarocas, C., & Van Alstyne, M. (2013). Money models for MOOCs. Communications of the ACM, 56(8), 25–28. doi:10.1145/2492007.2492017.

    Article  Google Scholar 

  • Domingos, P. (2005). Mining social networks for viral marketing. IEEE Intelligent Systems, 20(1), 80–82.

    Article  Google Scholar 

  • 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.

  • Frankola, K. (2001). Why online learners drop out. WORKFORCE-COSTA MESA, 80(10), 52–61.

    Google Scholar 

  • 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.

  • Gundecha, P., & Liu, H. (2012). Mining social media: a brief introduction. Tutorials in Operations Research, 1(4).

  • 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.

    Article  Google Scholar 

  • Han, J., Kamber, M., & Pei, J. (2006). Data mining: concepts and techniques. Morgan Kaufmann.

  • 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 

  • Huang, J., Dasgupta, A., Ghosh, A., Manning, J., & Sanders, M. (2014). Superposter behavior in MOOC forums.

  • Indra Devi, M., Rajaram, R., & Selvakuberan, K. (2008). Generating best features for web page classification. Webology, 5(1), 52.

    Google Scholar 

  • 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).

  • Kirkby, R., Frank, E., & Reutemann, P. (2006). WEKA explore user guide. Retrieved from http://www.cse.yorku.ca/course_archive/2006-07/W/4412/doc/weka/ExplorerGuide-3.5.5.pdf. Accessed 20 Jan 2014.

  • 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.

  • Lennon, C., & Burdick, H. (2004). The Lexile framework as an approach for reading measurement and success. http://www.lexile.com/research/1/. Accessed 20 Jan 2014.

  • Liaw, A., &Wiener, M. (2002). Classification and regression by randomForest. R News, 2/3:18–22. ISNN 1609-3631.

  • 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.

  • 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.

  • McAndrew, P. (2013). Learning from open design: running a learning design MOOC. eLearning Papers, (33).

  • 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.

  • MOOCs Mentor. (2013). MOOCs mentor releases dedicated MOOCs helpline, creates revolution. http://www.prlog.org/12261228-moocs-mentor-releases-dedicated-moocs-helpline-creates-revolution.html. Accessed 20 Jan 2014.

  • 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 

  • Norusis, M. (2008). SPSS 16.0 Guide to Data Analysis. NJ: Published by Prentice Hall Press Upper Saddle River. ISBN: 0136061362 9780136061366.

    Google Scholar 

  • Pappano, L. (2012). The year of the MOOC. New York Times. http://www.nytimes.com/2012/11/04/education/edlife/massive-open-online-courses-are-multiplying-at-a-rapid-pace.html?pagewanted=all&_r=0. Accessed 20 Jan 2014.

  • 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.

  • 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 

  • Rafter, M.V. (2013). MOOCs could lower cost of college. http://money.msn.com/personal-finance/moocs-could-lower-cost-of-college. Accessed 20 Jan 2014.

  • 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.

  • Robnik-Šikonja, M. (2004). Improving random forests. In Machine Learning: ECML 2004, pp. 359-370. Springer Berlin Heidelberg.

  • 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. http://www.eurodl.org/index.php?p=current&article=516. Accessed 20 Jan 2014.

  • Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. (2011). Handbook of educational data mining. Published by CRC Press, 2011.

  • Shah, D. (2013). MOOCs in 2013: breaking down the numbers. https://www.edsurge.com/n/2013-12-22-moocs-in-2013-breaking-down-the-numbers. Accessed 21 Jan 2014.

  • Shortell, T. (2001). Online textbook: an introduction to data analysis & presentation, Chapter 18: Correlations. http://www.shortell.org/book/chap18.html. Accessed 20 Jan 2014.

  • 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.

  • Thakur, M. (2007). The impact of ranking systems on higher education and its stakeholders. Journal of Institutional Research, 13(1), 83–96.

    Google Scholar 

  • 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).

  • 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 

  • Vrasidas, C., & McIsaac, M. S. (1999). Factors influencing interaction in an online course. American Journal of Distance Education, 13(3), 22–36.

    Article  Google Scholar 

  • Waldrop, M. M. (2013). Massive open online courses are transforming higher education—and providing fodder for scientific research. Nature, 495, 160–163.

    Article  Google Scholar 

  • Weka. (2013). Weka 3: Data Mining Software in Java. University of Waikato. http://www.cs.waikato.ac.nz/ml/weka/. Accessed 20 Jan 2014.

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Ionian University, Tsirigoti Square 7, 41900, Corfu, Greece

    Dimitrios Kravvaris, Katia Lida Kermanidis & Georgios Ntanis

Authors
  1. Dimitrios Kravvaris
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katia Lida Kermanidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Georgios Ntanis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dimitrios Kravvaris.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kravvaris, D., Kermanidis, K.L. & Ntanis, G. How MOOCs Link with Social Media. J Knowl Econ 7, 461–487 (2016). https://doi.org/10.1007/s13132-014-0219-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13132-014-0219-2

Keywords

Search

Navigation