College and university administrators are increasingly using
data not only from their own institutions but also from other, potentially
competing, schools to predict when their students might require an academic
intervention.
Observing and understanding data on common factors that
impact student retention and success—such as feeling isolated or overwhelmed,
selecting the wrong classes, or being unable to afford the next semester—enhances
administrators’ ability to proactively identify which students need help. For
example, using predictive-analysis processes developed by the University of
Texas at Austin, administrators at the University of Kansas discovered that
1,200 out of 1,500 students having difficulties on their campus hadn’t received
any kind of intervention.
Both schools are part of the three-year-old University Innovation Alliance, a consortium of 11 research universities dedicated to raising
undergraduate graduation rates. Since the group’s founding, its member
universities have managed to increase the number of degrees awarded by 10%,
with a 25% bump among Pell Grant recipients.
