Colleges Need to Use Predictive Data to Enhance—Not Hinder—Diversity

Manuela Ekowo wrote for EdSurge about the use of predictive data in college enrollment processes and how it can be used to create more diverse student populations.

Like most values, diversity does not come cheap. In an era of increasingly competitive college admissions, constrained resources, and the usual uncertainty about where students of all types will enroll, figuring out how and who to attract can be a challenge. Colleges have turned to predictive analytics—using past enrollment data to make predictions about future enrollment trends—to make tough decisions about who to actively recruit, admit, and support financially. In fact, a recent KPMG survey found that 41 percent of colleges are using their data to make predictions about future conditions and/or events.

Increasingly, colleges have proclaimed diversity as a value, and are committed to ensuring each incoming class is more diverse than the one before it. With underrepresented students disproportionately less likely to enroll in and finish college—and schools’ stated commitment to provide access to quality education for all—predictive analytics could enable colleges to focus their energies on recruiting the diverse students they say they want to attract. However, predictive data may be driving them to do just the opposite.

Predictive models attempt to create a profile of the “ideal student”—generally, a student that has successfully enrolled—and compare it against profiles of prospective students who have yet to be admitted. These models often include demographic data such as what high schools students attended as well as their ages, standardized test scores, race or ethnicities, and socioeconomic status. This information helps determine student profiles, which are assigned a score—1 is a low match to the ideal student, 10 is a high match.