Response neighborhoods in online learning networks: A quantitative analysis

Reuven Aviv, Zippy Erlich, Gilad Ravid

Research output: Contribution to journalArticlepeer-review


Theoretical foundation of Response mechanisms in networks of online learners are revealed by Statistical Analysis of p* Markov Models for the Networks. Our comparative analysis of two networks shows that the minimal-effort hunt-for-social-capital mechanism controls a major behavior of both networks: negative tendency to respond. Differences in designs of the networks enhance certain mechanisms while suppressing others: cognition balance, predicted by the theories of cognitive balance, and peer pressure, predicted by the theories of collective action are enhanced in a team like network but suppressed in a Q&A like forum. On the other hand, exchange mechanism, predicted by the theory of exchange & resource dependency and tutor's responsibility mechanism are enhanced in the Q&A type forum but suppressed in the team like network. Contagion mechanism, predicted by the theory of collective action did not develop in both networks. The different mechanisms lead to the formation of different micro and macro structures in the topologies of the responses of the networks and hence in the buildup of collaborative knowledge. The techniques presented in this work can be extended to other types of mechanisms and networks.

Original languageEnglish
Pages (from-to)90-99
Number of pages10
JournalEducational Technology and Society
Issue number4
StatePublished - 2005


  • Online Learning-Networks
  • Response-Neighborhoods
  • Social Network Analysis
  • p* analysis


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