Internet Learning Volume 3, Number 2, Fall 2014 | Page 74

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
With 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
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