Internet Learning Volume 3, Number 2, Fall 2014 | Page 79

Internet Learning
Figure 2. Examples of Data Intersectionalities.
erationalize and code the data. At the same
time, we feel a sense of urgency to understand
and improve social tools for learning,
and have worked and designed with the goal
of bringing research-based improvements to
online learning environments at scale. Our
ultimate goal is to advance the conversation
about social media in education, speed the
research-into-practice cycle, and support
the development of effective, efficient, engaging,
data-rich environments for social,
cooperative, and collaborative learning.
III - Research Questions
In order to conduct sophisticated analyses
of social interaction in online learning,
we determined that we must first
be able to identify, count, qualify and visualize
individual behaviors and interactions
among the network of participating faculty
and students. We also wanted to visualize
the traverse of anonymized faculty and student
conversations across the content map
of the course and program.
To this end, we formulated the following
high-level research questions:
• RQ1: Can we identify, differentiate,
and visualize individual characteristics
and behaviors in an online discussion
or course?
• RQ2: Can we identify, differentiate,
and visualize conversation characteristics
and behaviors in an online discussion
or course?
• RQ3: Can we identify and visualize
content focus over time in an online
discussion or course?
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