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

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.

[1]  Jimmy J. Lin,et al.  Visual analytics of MOOCs at maryland , 2014, L@S.

[2]  John R. Anderson,et al.  Locus of feedback control in computer-based tutoring: impact on learning rate, achievement and attitudes , 2001, CHI.

[3]  David E. Pritchard,et al.  Studying Learning in the Worldwide Classroom Research into edX's First MOOC. , 2013 .

[4]  Rebecca Ferguson,et al.  Social learning analytics: five approaches , 2012, LAK.

[5]  Deborah L. McGuinness,et al.  PROV-O: The PROV Ontology , 2013 .

[6]  Franck Dernoncourt,et al.  MOOCdb: Developing Standards and Systems to Support MOOC Data Science , 2014, ArXiv.

[7]  Erik Duval,et al.  Learning dashboards: an overview and future research opportunities , 2013, Personal and Ubiquitous Computing.

[8]  Zachary A. Pardos,et al.  moocRP: An Open-source Analytics Platform , 2015, L@S.

[9]  B. Bloom.  Learning for Mastery. Instruction and Curriculum. Regional Education Laboratory for the Carolinas and Virginia, Topical Papers and Reprints, Number 1. , 1968 .

[10]  Kenneth R. Koedinger,et al.  A Data Repository for the EDM Community: The PSLC DataShop , 2010 .

[11]  Ryan Shaun Joazeiro de Baker,et al.  Educational Data Mining and Learning Analytics: Applications to Constructionist Research , 2014, Technology, Knowledge and Learning.

[12]  Stacy Marsella,et al.  Computationally modeling human emotion , 2014, CACM.

[13]  Neil T. Heffernan,et al.  The ASSISTments Ecosystem: Building a Platform that Brings Scientists and Teachers Together for Minimally Invasive Research on Human Learning and Teaching , 2014, International Journal of Artificial Intelligence in Education.

[14]  Marsha C. Lovett,et al.  The Open Learning Initiative: Measuring the Effectiveness of the OLI Statistics Course in Accelerating Student Learning. , 2008 .

[15]  Dirk Ifenthaler,et al.  Development and Validation of a Learning Analytics Framework: Two Case Studies Using Support Vector Machines , 2014, Technol. Knowl. Learn..

[16]  Justin Reich,et al.  Privacy, Anonymity, and Big Data in the Social Sciences , 2014 .

[17]  Sidney D'Mello,et al.  Data mining and education. , 2015, Wiley interdisciplinary reviews. Cognitive science.

[18]  Sean Bechhofer,et al.  SKOS Simple Knowledge Organization System Reference , 2009 .

[19]  John R. Anderson,et al.  Cognitive Tutors: Lessons Learned , 1995 .