Advertisement

Technology, Knowledge and Learning

, Volume 21, Issue 1, pp 75–98 | Cite as

moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization

  • Zachary A. Pardos
  • Anthony Whyte
  • Kevin Kao
Article
First Online:

Abstract

In this paper, we address issues of transparency, modularity, and privacy with the introduction of an open source, web-based data repository and analysis tool tailored to the Massive Open Online Course community. The tool integrates data request/authorization and distribution workflow features as well as provides a simple analytics module upload format to enable reuse and replication of analytics results among instructors and researchers. We survey the evolving landscape of competing established and emerging data models, all of which are accommodated in the platform. Data model descriptions are provided to analytics authors who choose, much like with smartphone app stores, to write for any number of data models depending on their needs and the proliferation of the particular data model. Two case study examples of analytics and responsive visualizations based on different data models are described in the paper. The result is a simple but effective approach to learning analytics immediately applicable to X consortium MOOCs and beyond.

Keywords

Open learning analytics Modularization MOOC Dashboards Visualizations Reproducible research 
This is a preview of subscription content, log in to check access.

References

  1. Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167–207.CrossRefGoogle Scholar
  2. Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1–2), 205–220.CrossRefGoogle Scholar
  3. Bloom, B. S. (1968). Learning for mastery. Instruction and curriculum. Regional education laboratory for the Carolinas and Virginia, topical papers and reprints, Number 1. Evaluation Comment, 1(2), 1–12.Google Scholar
  4. Breslow, L., Pritchard, D. E., DeBoer, J., Stump, G. S., Ho, A. D., & Seaton, D. T. (2013). Studying learning in the worldwide classroom: Research into edX’s first MOOC. Research & Practice in Assessment, 8, 13–25.Google Scholar
  5. Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In Proceedings of the SIGCHI conference on human factors in computing systems (pp. 245–252), ACM.Google Scholar
  6. Daries, J. P., Reich, J., Waldo, J., Young, E. M., Whittinghill, J., Ho, A. D., & Chuang, I. (2014). Privacy, anonymity, and big data in the social sciences. Communications of the ACM, 57(9), 56–63.CrossRefGoogle Scholar
  7. Ferguson, R., & Shum, S. B. (2012). Social learning analytics: Five approaches. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 23–33), ACM.Google Scholar
  8. Heffernan, N. T., & Heffernan, C. L. (2014). The ASSISTments Ecosystem: Building a platform that brings scientists and teachers together for minimally invasive research on human learning and teaching. International Journal of Artificial Intelligence in Education, 24(4), 470–497.CrossRefGoogle Scholar
  9. Ifenthaler, D., & Widanapathirana, C. (2014). Development and validation of a learning analytics framework: Two case studies using support vector machines. Technology, Knowledge and Learning, 19(1–2), 221–240.CrossRefGoogle Scholar
  10. Koedinger, K. R., Baker, R. S., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. Handbook of Educational Data Mining, 43–56.Google Scholar
  11. Koedinger, K. R., D’Mello, S., McLaughlin, E. A., Pardos, Z. A., & Rosé, C. P. (2015). Data mining and education. WIREs Cognitive Science, 6, 333–353. doi: 10.1002/wcs.1350.CrossRefGoogle Scholar
  12. Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., & et al. (2013). Prov-o: The prov ontology. W3C Recommendation, 30.Google Scholar
  13. Lovett, M., Meyer, O., & Thille, C. (2008). The Open Learning Initiative: Measuring the effectiveness of the OLI statistics course in accelerating student learning. Journal of Interactive Media in Education, no. 14, JIME Special Issue: Researching Open Content in Education. http://jime.open.ac.uk/2008/14.
  14. Miles, A., & Bechhofer, S. (2009). SKOS simple knowledge organization system reference. W3C recommendation, 18, W3C. http://www.w3.org/TR/2009/REC-skos-reference-20090818.
  15. Pardos, Z. A., & Kao, K. (2015). moocRP: An open-source analytics platform. In Proceedings of the Second (2015) ACM conference on learning@ scale (pp. 103–110), ACM.Google Scholar
  16. Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Buckingham Shum, S., & Ferguson, R. (2011). Open learning analytics: An integrated and modularized platform (Concept Paper). SOLAR.Google Scholar
  17. Veeramachaneni, K., Halawa, S., Dernoncourt, F., O’Reilly, U. M., Taylor, C., & Do, C. (2014). MOOCdb: Developing standards and systems to support mooc data science. arXiv preprint arXiv:1406.2015.
  18. Verbert, K., Govaerts, S., Duval, E., Santos, J. L., Van Assche, F., Parra, G., & Klerkx, J. (2014). Learning dashboards: An overview and future research opportunities. Personal and Ubiquitous Computing, 18(6), 1499–1514.Google Scholar
  19. Xu, Z., Goldwasser, D., Bederson, B. B., & Lin, J. (2014). Visual analytics of MOOCs at maryland. In Proceedings of the first ACM conference on learning@ scale conference (pp. 195–196).Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.University of California at BerkeleyBerkeleyUSA
  2. 2.University of MichiganAnn ArborUSA

Personalised recommendations

Cite article

Buy options