Communities of performance & communities of preference

Rebecca Brown, Collin Lynch, Yuan Wang, Michael Eagle, Jennifer Albert, Tiffany Barnes, Ryan Baker, Yoav Bergner, Danielle McNamara

Research output: Contribution to journalConference article

Abstract

The current generation ofMassiveOpenOnline Courses (MOOCs) operate under the assumption that good students will help poor students, thus alleviating the burden on instructors and Teaching Assistants (TAs) of having thousands of students to teach. In practice, this may not be the case. In this paper, we examine social network graphs drawn from forum interactions in a MOOC to identify natural student communities and characterize them based on student performance and stated preferences. We examine the community structure of the entire course, students only, and students minus low performers and hubs. The presence of these communities and the fact that they are homogeneous with respect to grade but not motivations has important implications for planning in MOOCs.

Original languageEnglish (US)
JournalCEUR Workshop Proceedings
Volume1446
StatePublished - 2015
Event8th International Conference on Educational Data Mining, EDM 2015 - Madrid, Spain
Duration: Jun 26 2015Jun 29 2015

Keywords

  • Community detection
  • MOOC
  • Online forum
  • Social network

ASJC Scopus subject areas

  • Computer Science(all)

Fingerprint Dive into the research topics of 'Communities of performance & communities of preference'. Together they form a unique fingerprint.

  • Cite this

    Brown, R., Lynch, C., Wang, Y., Eagle, M., Albert, J., Barnes, T., Baker, R., Bergner, Y., & McNamara, D. (2015). Communities of performance & communities of preference. CEUR Workshop Proceedings, 1446.