Internet Learning Volume 6, Number 1, Spring 2017/Summer 2017 | Page 11

Internet Learning
search and strategies for adoption that
help bring data-driven innovation to all
members of the college community.
In the sections that follow, the
Predictive Analytics Reporting (PAR)
framework is introduced and its creation
and dissemination of common
data definitions and a shared structure
for inventorying and testing student
success interventions are discussed.
Descriptions of how common frameworks
can inform the scaling of findings
cross-institutionally with specific
examples from PAR research, and a
general discussion of how PAR tools
can be used is included. Finally, observations
on what PAR can tell us about
the adoption of a disruptive technology
are shared.
Improving Student Success
Nationally, colleges and universities
struggle to improve student
success; improvements
have been especially challenging for
realizing improvements with the lowest
socioeconomic groups (Shapiro et al.,
2014). Demands for improvement have
resulted in a stronger focus on exploring
student outcomes, including college
completion. The scrutiny of outcomes
has contributed to both an expanding
market for educational technology that
addresses outcome issues, and the internal
institutional drive to innovate in
the area of support for student success.
These two trends set the stage for institutions
to leverage academic and learning
analytics (Norris & Baer, 2013). The
educational technology marketplace
responded by creating tools, products,
and services designed to serve the needs
of individual institutions.
A different approach to improving
innovation to optimize student
success is to work through a community
of practice. The Predictive Analytics
Reporting (PAR) Framework was a
project originally funded by the Bill &
Melinda Gates Foundation and guided
by a management team from the Western
Interstate Commission for Higher
Education (WICHE) Cooperative for
Educational Technologies (WCET) (Ice
et al., 2012). The PAR Framework later
became a nonprofit, multi-institutional
collaborative that provided member
institutions with tools and resources for
identifying risks and improving student
success. The assets of the not-for-profit
PAR Framework were acquired by
Hobsons in 2016, with the intention of
continuing to support member-driven
collaborations that help institutions
and systems through the combined
power of a collective dataset, analytic
tools to improve member metrics, and
research-based approaches to identifying
student success interventions.
Common Data Definitions
The goal of the six founding institutions
that participated in
the original PAR Framework
discussions was to demonstrate that it
was possible to use predictive analytics
to find students at risk of dropping
out of college. To do this work, the PAR
Framework created a single, federated,
cross-institutional dataset to investi-
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