As networking platforms have become more ubiquitous in the personal consumer space, data derived from social interaction is increasingly being used in the commercial space to analyze markets, make decisions, and develop new, personalized tools. However, even as social tools and design develop a presence in the learning space, research using social data to develop new understandings about knowledge production, teaching, and learning in online social learning spaces is fairly limited. This article is a practitioners’ progress report on a research collaboration between Columbia University School of Continuing Education and Pearson Higher Education Technology, established with the goal of developing a framework and methodology for studying how social interactions and knowledge construction unfold in online courses that employ both formal and informal social learning activities. The work describes an emergent methodology for analyzing data produced by social and conversational interactions in online learning environments, using threaded discussion data from a group of students and faculty at Columbia University School of Continuing Education. It overviews the graph database schema and technologies employed, and describes examples of how the data is used to describe, differentiate among, and visualize individuals, conversations, and patterns of concept connectedness. Finally, it discusses relative strengths and weaknesses of the approach, suggesting ways it might evolve to improve our understanding of social networking and engagement in online learning environments, and how it can optimally impact student learning.
Baker-Stein, Marni; York, Sean; and Dashew, Brian
"Visualizing Knowledge Networks in Online Courses,"
Internet Learning: Vol. 3
, Article 8.
Available at: http://digitalcommons.apus.edu/internetlearning/vol3/iss2/8
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