Studying MOOC completion at scale using the MOOC replication framework

Juan Miguel L. Andres, Ryan S. Baker, George Siemens, Catherine A. Spann, Dragan Gašević, Scott Crossley

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

1 Citation (Scopus)

Abstract

Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.

Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Educational Data Mining
EditorsX. Hu, T. Barnes, A. Hershkovitz, L. Paquette
Place of PublicationChina
PublisherInternational Educational Data Mining Society
Pages338-339
Number of pages2
Publication statusPublished - 2017
Externally publishedYes
EventEducational Data Mining 2017 - Central China Normal University, Wuhan, China
Duration: 25 Jun 201728 Jun 2017
Conference number: 10th
http://educationaldatamining.org/EDM2017/

Conference

ConferenceEducational Data Mining 2017
Abbreviated titleEDM 2017
CountryChina
CityWuhan
Period25/06/1728/06/17
Internet address

Keywords

  • Meta-analysis
  • MOOC
  • MORF
  • Replication

Cite this

Andres, J. M. L., Baker, R. S., Siemens, G., Spann, C. A., Gašević, D., & Crossley, S. (2017). Studying MOOC completion at scale using the MOOC replication framework. In X. Hu, T. Barnes, A. Hershkovitz, & L. Paquette (Eds.), Proceedings of the 10th International Conference on Educational Data Mining (pp. 338-339). International Educational Data Mining Society. http://educationaldatamining.org/EDM2017/proceedings-full/