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Using weak ties to understand the resource usage and sharing patterns of a professional learning community

  • Ogheneovo DibieEmail author
  • Tamara Sumner
Original Article
  • 465 Downloads

Abstract

This research demonstrates the utility of the theory of weak ties for understanding the patterns of resource usage and sharing in an online professional learning community. Our context of study is a community of educators using and sharing teaching resources such as lesson plans, presentation slides and animations. We consider whether the deduced relationships between members of the community of educators constitute weak ties. A deduced relationship exists when two educators access the same resource. If these deduced relationships do constitute weak ties, then other theorized network properties should also be manifest, namely homophily and triadic closures. Our findings support these theoretical conjectures. Firstly, results indicate that the strength of a tie is directly proportional to the level of similarity between users in the network in terms of their propensity to use and share resources and their level of comfort with and use of technology (homophily property). Secondly, we found strong support for the triadic closure property (formation of a weak tie between unconnected nodes that share a common neighbor). Thus, we developed a computational model to predict the formation of weak ties via triadic closures with an accuracy of 97.8 %. Finally, we show that augmenting collaborative and hybrid recommender systems with our triadic closure prediction model can improve the performance of these systems.

Keywords

Social network analysis Triadic closures Homophily Collaborative-filtering Content-based Recommendation systems 

Notes

Acknowledgments

This paper is based upon research supported by National Science Foundation awards #1043638 and #1147590 of the University of Colorado Boulder and the University Corporation for Atmospheric Research.

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Copyright information

© Springer-Verlag Wien 2016

Authors and Affiliations

  1. 1.Institute of Cognitive Science, Department of Computer ScienceUniversity of Colorado BoulderBoulderUSA

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