University Business May 2017 - 24

SPONSORED WEB SEMINAR DIGEST

Student Success
Beyond the First Year
Supporting students throughout their college careers
A University Business Web Seminar Digest * Originally presented on March 9, 2017

Often, student success efforts are focused primarily on retaining first-year students, but fail to continue
supporting students throughout their college careers. At the University of North Carolina-Greensboro,
the institution's leadership wanted to take a broader approach to student success by developing a
predictive model that would include upperclassmen.
In this web seminar, the vice chancellor for enrollment management and the data manager from
UNC-Greensboro discussed the collaborative approach the institution took in developing a full retention
predictive model, which now provides the probability of retention for freshmen, sophomores and juniors
as well as incoming freshmen, and some best practices for using predictive analytics to support student
success at any institution.
Dr. Bryan Terry: Enrollment management is about paying attention to details. When new students come in,
we know some are sure to succeed. They're asking the
right questions, they want to be Spartans, they want
to graduate as Spartans. But we know we have others who are going to struggle. So one of the things
we wanted to do-because we don't have the budget
to buy a $250,000 software package that will solve all
of our problems, or to hire a counselor for each student-was to take our limited resources and pair them
against those students who need the academic advisement the most.
That's what Rapid Insight did for us. It was a very
low-cost option that allowed us to be a data-driven institution. So far it's been very successful.
Jon MacMillan: Rapid Insight is a predictive analytics software company founded in 2002. We are in a
few different markets, but our main focus has been
higher education, serving over 200 institutions nationwide, from small to large, private and public,
community colleges, law schools, four-year institutions and so on. Our whole goal is to make data analytics predictive modeling very easy and intuitive for
users of any ability.

One way we do that is we create software that fits
all customers' needs. So we have powerful data analytics software for data scientists, but if you lack coding skills and statistical knowledge, the products are
still intuitive, allowing users of all abilities the option
to perform these different analyses. We also have an
open services support model-if you have a question,
we're always here to answer.
Then on top of that we have QuickStart templates.
We partnered with UNC-Greensboro to develop a template for actually creating this data to build these predictive models.
We provide software that allows you to integrate
data from any source. That may be pulling directly
from your student information system or other databases, and then cleaning that up, performing statistical functions, creating new metrics based on the
information you have, and then automatically identifying the key indicators of whatever outcome you're
interested in.
Jeffrey Collis: We have two different models that were
our first models to be established. The first one "likelihood to enroll," and then we also wanted "likelihood to
retain." With incoming classes of 2,500 to 3,000 first-

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