Abstract
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 language | English (US) |
|---|---|
| Pages (from-to) | 93-122 |
| Number of pages | 30 |
| Journal | International Review of Research in Open and Distance Learning |
| Volume | 23 |
| Issue number | 4 |
| DOIs | |
| State | Published - Nov 2022 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Education
Keywords
- Information theory
- Network analysis
- Sloan
- Social cognitive theory
- Social learning
- Social learning optimization analysis of networks