Deans

H. Wells Singleton – Abraham S. Fischler School of Education Richard Davis – College of Health Care Sciences William Hardigan – College of Pharmacy Associate Vice President Virginia McLain – Office of Information Technology

Award Date

1-1-2005

Abstract

The purpose of this study is to use social network analysis to gain a greater understanding of how online learning communities can be used to enhance the delivery of online instruction. To achieve this goal, this project will investigate how online learning communities are created and evolve over time. More specifically, the study will investigate how information flows and network structure can be used to gain a greater understanding of online communities. Since communication, the stock in trade of education, is "undergoing a radical transformation," then the way in which we study communication must change (Monge & Contractor, 2003). Although the number of courses offered over the Internet has grown dramatically during the past five years, nearly all of the research on the effectiveness of online courses has been either anecdotal or based on a comparison of test scores and student evaluations. With few exceptions, educators have not investigated the complex patterns of communication that take place in an online learning community. As a result, educators know very little about how online cliques, gatekeepers, mentors, or network structure impact the online learning environment. This project will study three online communities: international pharmacy students, physical therapy doctoral students, and education doctoral students and alumni. The study will map the network structure of each group of students and its evolution during the next year. Data will be collected on the overall pattern of relations, the centrality of different roles, and the structure of the network. Changes in the network structure will be monitored to determine if changes in instruction, student roles, or mentor roles are reflected in the structure of the social network. The data collected from the network analysis, interviews, and focus groups will then be analyzed for common themes, contradictions, or confirmatory support. NUD*IST will be used to store and analyze the focus group data. GRADAP will be used to analyze the social network analysis. The data collected from the focus groups, interviews, and the network analysis data will then be compared and analyzed by the team members to identify common themes as well as ideas for additional research.

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