SLOAN: Social Learning Optimization Analysis of Networks

David John Lemay, Tenzin Doleck, Christopher G. Brinton

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

Original languageEnglish (US)
Pages (from-to)93-122
Number of pages30
JournalInternational Review of Research in Open and Distance Learning
Issue number4
StatePublished - Nov 2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Education


  • Information theory
  • Network analysis
  • Sloan
  • Social cognitive theory
  • Social learning
  • Social learning optimization analysis of networks


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