When using data to predict outcomes, consider the ethical dilemmas, new report urges
Researchers say colleges need to take care in how they use predictive analytics
Colleges and universities that use sophisticated data systems to analyze and guide students must be careful to avoid ethical dilemmas that can arise, like pigeonholing students unfairly or underestimating their abilities, according to a new report that analyzed common pitfalls of these systems.
These systems, known as predictive analytics, have the potential to reinforce disparities for disadvantaged students if used without proper training and consideration for how computers might shuffle students into categories, according to the report from New America, a Washington, D.C.-based think tank. The report was released this week at SXSWedu, a national education technology conference in Austin, Texas.“We should always try to balance the potential of technology with its risks,” said Manuela Ekowo, an education policy analyst at New America, who co-authored the report with Iris Palmer, a senior policy analyst. “We shouldn’t let technology blindfold us to the ways it can do harm, particularly to students from underserved and under-represented backgrounds.”
Predictive analytics uses data to help inform instruction or academic guidance for students. It takes past performance, such as grades or other data points thought to be correlated with academic success, and attempts to distill that data into reports that can help ensure a student stays on track for graduation. It is a growing trend in higher education but it’s not ubiquitous. A report last fall from the same authors estimated that less than half of higher education institutions surveyed use data in this way.
Some say these methods can help improve student outcomes. Georgia State University, for instance, uses this type of a program to