Response Neighborhoods in Online Learning Networks: A Quantitative Analysis

Response mechanisms and neighborhoods in networks of online learners are revealed by Statistical Analysis of p* Markov Models for the Networks. Our analysis shows that the minimal-effort hunt-for-social-capital mechanism controls a major behavior of the networks: negative tendency to respond. Differences in the goals, interdependence and pre-assigned roles features of the designs lead to the development of different mechanisms: cognition balance and peer pressure in a team like network, exchange mechanism in a Q&A type forum. These differences lead to the formation of transitive and star-like response triads in the team network and mutual dyads in the Q&A forum. These micro structures lead to differences in the macro-structures of the networks and the buildup of collaborative knowledge. The techniques presented in this work can be extended to other types of mechanisms and networks.