The civic mission of MOOCs: Measuring engagement across political differences in forums

Justin Reich, Brandon Michael Stewart, Kimia Mavon, Dustin Tingley

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

17 Scopus citations

Abstract

In this study, we develop methods for computationally measuring the degree to which students engage in MOOC forums with other students holding different political beliefs. We examine a case study of a single MOOC about education policy, Saving Schools, where we obtain measures of student education policy preferences that correlate with political ideology. Contrary to assertions that online spaces often become echo chambers or ideological silos, we find that students in this case hold diverse political beliefs, participate equitably in forum discussions, directly engage (through replies and upvotes) with students holding opposing beliefs, and converge on a shared language rather than talking past one another. Research that focuses on the civic mission of MOOCs helps ensure that open online learning engages the same breadth of purposes that higher education aspires to serve.

Original languageEnglish (US)
Title of host publicationL@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages1-10
Number of pages10
ISBN (Electronic)9781450337267
DOIs
StatePublished - Apr 25 2016
Event3rd Annual ACM Conference on Learning at Scale, L@S 2016 - Edinburgh, United Kingdom
Duration: Apr 25 2016Apr 26 2016

Other

Other3rd Annual ACM Conference on Learning at Scale, L@S 2016
CountryUnited Kingdom
CityEdinburgh
Period4/25/164/26/16

All Science Journal Classification (ASJC) codes

  • Education
  • Software
  • Computer Science Applications
  • Computer Networks and Communications

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    Reich, J., Stewart, B. M., Mavon, K., & Tingley, D. (2016). The civic mission of MOOCs: Measuring engagement across political differences in forums. In L@S 2016 - Proceedings of the 3rd 2016 ACM Conference on Learning at Scale (pp. 1-10). Association for Computing Machinery, Inc. https://doi.org/10.1145/2876034.2876045