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

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.

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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/.

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Correspondence to Zachary A. Pardos.

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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

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Keywords

  • Open learning analytics
  • Modularization
  • MOOC
  • Dashboards
  • Visualizations
  • Reproducible research