Discourse: MOOC discussion forum analysis at scale

Alexander Kindel, Michael Yeomans, Justin Reich, Brandon Michael Stewart, Dustin Tingley

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

1 Scopus citations

Abstract

We present Discourse, a tool for coding and annotating MOOC discussion forum data. Despite the centrality of discussion forums to learning in online courses, few tools are available for analyzing these discussions in a context-Aware way. Discourse scaffolds the process of coding forum data by enabling multiple coders to work with large amounts of forum data. Our demonstration will enable attendees to experience, explore, and critique key features of the app.

Original languageEnglish (US)
Title of host publicationL@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale
PublisherAssociation for Computing Machinery, Inc
Pages141-142
Number of pages2
ISBN (Electronic)9781450344500
DOIs
StatePublished - Apr 12 2017
Event4th Annual ACM Conference on Learning at Scale, L@S 2017 - Cambridge, United States
Duration: Apr 20 2017Apr 21 2017

Publication series

NameL@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale

Other

Other4th Annual ACM Conference on Learning at Scale, L@S 2017
CountryUnited States
CityCambridge
Period4/20/174/21/17

All Science Journal Classification (ASJC) codes

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

Keywords

  • Content analysis
  • Discussions
  • MOOCs
  • Reply mapping

Fingerprint Dive into the research topics of 'Discourse: MOOC discussion forum analysis at scale'. Together they form a unique fingerprint.

  • Cite this

    Kindel, A., Yeomans, M., Reich, J., Stewart, B. M., & Tingley, D. (2017). Discourse: MOOC discussion forum analysis at scale. In L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale (pp. 141-142). (L@S 2017 - Proceedings of the 4th (2017) ACM Conference on Learning at Scale). Association for Computing Machinery, Inc. https://doi.org/10.1145/3051457.3053967