Internet Learning, Volume 1, Number 1 Volume 3, Number 2, Fall 2014 - Page 73

Internet Learning Volume 3 Number 2 - Fall 2014 Visualizing Knowledge Networks in Online Courses Marni Baker-Stein, Sean York & Brian Dashew 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. Keywords: Social, analytics, knowledge, networks, visualization Note: The figures labeled as Interactive may be viewed by downloading the Internet Learning Journal app from the iOS App Store. I - Introduction W ith the rise of consumer-facing networking platforms like Facebook, Twitter, LinkedIn and Instagram, “social” has become a dynamic engine of commercial enterprise powered by huge amounts of data. This data is structured and presented in ways that drive the continuous development of real time, highly personalized tools for social and professional networking. And, perhaps even more critically, it has the potential to give us unprecedented insights into the social mechanisms that underpin cultural practices of learning and knowledge production. However in research efforts targeted at understanding student success and learning in higher education and specifically in online courses and programs, we have only recently begun to explore the potential uses and impacts of ”social”. Learning management systems that support online instruction increasingly provide (or integrate 73