Date of Award
Doctor of Philosophy (PhD)
Online learning communities are becoming an invaluable component of educator instruction. By providing educators with access to teaching resources and best practices shared by their peers, these communities have been shown to improve the instructional practices of educators and produce increases in student learning. Given the importance of online learning communities to teaching and learning, understanding their dynamics and the factors that influence these dynamics has key implications for educator instruction and student learning. A better understanding of the aforementioned dynamics can also benefit agencies that support these communities.
In this dissertation, I show that sociological network theory can be used to understand the dynamics of online learning communities. Specifically, the phenomena of homophily (tendency of individuals to have social ties with others of similar traits) and triadic closures (tendency of new connections to develop between individuals sharing a common neighbor) can be understood through the sharing and usage behaviors of educators. I also demonstrate how an understanding of the triadic closure process can be used to improve the performance of traditional resource recommendation systems. Finally, I show that social influence may play a significant role in the diffusion and popularity of resources within online learning communities.
Dibie, Ogheneovo, "Computational Methodologies for Understanding the Dynamics of an Online Community of Educators" (2016). Computer Science Graduate Theses & Dissertations. 117.