Internet Learning Volume 3, Number 2, Fall 2014 | Page 75

Internet Learning
with) social networking tools to facilitate
community building and social knowledge
networking. Yet related research efforts
that seek to understand student behavior in
online courses have focused primarily on
attendance patterns and wayfinding behaviors,
content engagement and assessment
outcomes, leaving the social dimensions of
these environments relatively unexplored.
It is often said that we value what we
can measure, and we measure what we value.
A review of the technology impacting
the state of higher education instruction
and research indicates both value and measurability
may be shifting towards an examination
of the social space as a powerful
means of surfacing knowledge construction
activity. The 2014 Horizons Report
(New Media Consortium, 2014) lists the
growing ubiquity of social media as among
the drivers of change likely to impact education
within the next two years. The report
also lists two trends as three to five years
away from having a significant impact on
the state of higher education: the rise of data-driven
learning and assessment, and a
shift towards viewing students as creators of
content. We believe these and other trends
listed in the Horizons Report indicate the
time is now to gain insights into the conditions
that promote social knowledge networking
in online courses and to identify
practical methods to measure its impacts.
With these goals in mind in 2011 the researchers
launched a collaborative research
effort between the Columbia University
School of Continuing Education program
development and instructional design
team, and Pearson Higher Education Technology.
Together, we defined an exploratory
methodology and an initial set of logical
questions to guide research-engaging data
produced from the social networking environment
of an online master’s degree program
offered at Columbia University. Our
goal was to develop a framework and methodology
aimed broadly at allowing us to
better understand social interactions and
knowledge construction in online courses
that employ both formal and informal social
and cooperative learning activities.
We will first elaborate our definition
of Social Knowledge Networking (SKN)
and the logic we applied in structuring our
data and identifying the initial questions
that grounded our research. Next, we provide
a generic description of our emergent
methodology for analyzing the data produced
by social and conversational interactions
in online learning environments.
Then we present an overview of the graph
schema and technologies we used, followed
by results for each of our three research
questions. Finally, we discuss relative
strengths and weaknesses of the method,
suggesting ways it might evolve to improve
our understanding of how social networking
and engagement work in online learning
environments and how it can optimally
impact student learning.
II - Analytical Framework
Our initial analytical framework incorporated
relevant concepts from
content analysis, knowledge network
analysis, and conversational analysis
into a custom model, represented in Figure
1.
A. The Knowledge Map
Foundational to this framework is the
recognition that each course contains
an underlying knowledge map. The
map represents the conceptual skeleton of
the course, including those concepts provided
by the instructor via course resources,
lectures, or activity prompts, and those introduced
via discussion in the course. Part
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