Evolving Models for Predictive Analytics and Student Success

Predictive analytics has played an important role in helping colleges and universities support students. But the pandemic has deeply changed how students engage with faculty members and their institutions.

As a result, predictive-analytics models, too, have changed. How can colleges adapt their algorithms to better predict enrollment, retention, and completion, and the factors that may affect those results?

A panel of higher-ed experts convenes to discuss how colleges can develop new data models and strategies to keep students on the path to a degree, including such questions as:

• How did institutions adapt their algorithms in light of the pandemic?
• How does collecting student data change in a remote environment versus an in-person one?
• What do students need now from their colleges to keep them enrolled and moving forward?