Evaluating the efficiency of social learning networks: Perspectives for harnessing learning analytics to improve discussions

Tenzin Doleck, David John Lemay, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

This study evaluates the validity of an algorithm for measuring the efficiency of social learning networks in discussion forums accompanying MOOCs of conventional format, which consist of video lectures and problem assignments. The algorithm models social learning networks as a function of users knowledge seeking and knowledge disseminating tendencies across course topics and offers a means to optimize social learning networks by connecting users seeking and disseminating information on specific topics. We use the algorithm to analyze the social learning network manifest in the discussion format of a MOOC forum incorporating video lectures and problem assignments. As a measure of the degree that knowledge seekers and knowledge disseminators are connected in the network, we observe a very sparse network with few discussion participants and a limited range of topics. Hence, only small gains are available through optimization, since for a very sparse network, few connections can be made. The development of a metric for the analysis of social learning networks would provide instructors and researchers with a means to optimize online learning environments for empowering social learning. Finally, we discuss our findings with respect to the potential of self-optimizing discussion forums for supporting social learning online.

Original languageEnglish (US)
Article number104124
JournalComputers and Education
Volume164
DOIs
StatePublished - Apr 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Education

Keywords

  • Cooperative/collaborative learning
  • Data science applications in education
  • Distance education and online learning
  • Informal learning

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