Skip to main content
SpringerLink
Log in

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

  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

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.

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
Fig. 13
Fig. 14

Similar content being viewed by others

Notes

  1. https://github.com/CAHLR/moocRP.

  2. The Asilomar Convention for Learning Research in Higher Education (http://asilomar-highered.info/asilomar-convention-20140612.pdf).

  3. edX Research Guide. Data Delivered in Data Packages. http://edx.readthedocs.org/projects/devdata/en/latest/internal_data_formats/package.html, 2014.

  4. Jim Waldo. HarvardX Tools. http://github.com/jimwaldo/HarvardX-Tools.

  5. ADL. http://github.com/adlnet/-xAPI-Spec/blob/master/xAPI.md.

  6. Stanford Vice Provost Office for Online Learning. How to Access the VPOL Online Learning Data. http://datastage.stanford.edu.

  7. Andreas Paepcke. json_to_relation. http://github.com/paepcke/json_to_relation.

  8. MOOCdb. http://moocdb.csail.mit.edu/wiki/index.php?title=MOOCdb.

  9. Advanced Distributed Learning Initiative (ADL), U.S. Department of Defense. http://adlnet.gov/adl-research/performance-tracking-analysis/experience-api/xapi-background-history/.

  10. IMS Global Learning Consortium (IMS). “Learning Measurement for Analytics Whitepaper (2013). http://www.imsglobal.org/sites/default/files/caliper/IMSLearningAnalyticsWP.pdf.

  11. ADL. http://adlnet.gov/adl-research/performance-tracking-analysis/experience-api/xapi-technical-specifications/. See also http://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md#roleofxapi.

  12. Andy Whitaker, “An Introduction to the Tin Can API”, The Training Business (19 July 2012). http://www.thetrainingbusiness.com/softwaretools/tin-can-api/.

  13. ADL. http://adlnet.gov/adl-research/performance-tracking-analysis/experience-api/.

  14. IMS. http://www.imsglobal.org/article/ims-global-learning-consortium-announces-products-certified-newly-released-caliper.

  15. ADL. http://github.com/adlnet/xAPI-Spec/blob/master/xAPI.md#stmtprops.

  16. See http://adlnet.gov/adl-research/performance-tracking-analysis/experience-api/xapi-community-of-practice-cop/.

  17. xAPI Badges CoP. http://github.com/ht2/BadgesCoP/blob/master/earning/vocab.md.

  18. xAPI Course CoP. http://github.com/adlnet/xAPI-SCORM-Profile/blob/master/xapi-scorm-profile.md; xAPI Social CoP. http://docs.google.com/document/d/1RpFxEh0KdO6WGgK74LUctP5oM35nsWHk0Czk__syH1Q/edit; xAPI Video CoP. http://docs.google.com/spreadsheets/d/1jq2zrvv2LKsE6-vbSBCc6H-PCyn40dQA4P96bl3s6BI/edit-gid=0.

  19. ADL. http://w3id.org/xapi/adl/; WordNet, Princeton University. http://wordnet-rdf.princeton.edu/.

  20. A number of these issues are expected to be resolved in the upcoming Caliper 1.1 release.

  21. ADL Technical Team, http://docs.google.com/document/d/1zBPKryuF1tXHTI-AYjXd0ctdWoq4o4P-Uq9SAhJfus0/edit?pli=1#.

  22. xAPI Vocabulary & Semantic Interoperability Group. http://www.w3.org/community/xapivocabulary/. See also http://github.com/adlnet/xapi-vocabulary.

  23. ADL. http://xapi.vocab.pub/ontology/index.html.

  24. Unizin Consortium. http://unizin.org/2015/11/unizin-consortium-partners-with-ims-global-learning-consortium-to-drive-caliper-analytics-adoption/.

  25. Unizin, the Apereo Foundation and the UK’s JISC have all outlined plans to build standards-based learning analytics infrastructures in partnership with commercial vendors. See http://unizin.org/, http://www.apereo.org/communities/learning-analytics-initiative, http://analytics.jiscinvolve.org/wp/2015/06/15/jiscs-learning-analytics-architecture-whos-involved-what-are-the-products-and-when-will-it-be-available/.

References

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Lebo, T., Sahoo, S., McGuinness, D., Belhajjame, K., Cheney, J., Corsar, D., & et al. (2013). Prov-o: The prov ontology. W3C Recommendation, 30.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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.

  • 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 

  • 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).

Download references

Author information

Authors and Affiliations

  1. University of California at Berkeley, Berkeley, CA, 94720, USA

    Zachary A. Pardos & Kevin Kao

  2. University of Michigan, Ann Arbor, MI, USA

    Anthony Whyte

Authors
  1. Zachary A. Pardos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony Whyte
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kevin Kao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zachary A. Pardos.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pardos, Z.A., Whyte, A. & Kao, K. moocRP: Enabling Open Learning Analytics with an Open Source Platform for Data Distribution, Analysis, and Visualization. Tech Know Learn 21, 75–98 (2016). https://doi.org/10.1007/s10758-015-9268-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-015-9268-2

Keywords

Search

Navigation