Experts say a good predictive-analytics system avoids making recommendations based primarily on a student’s financial or cultural background. In Milliron’s experience, many colleges initially assume it’s enough just to observe that low-income students or those who belong to certain racial groups underperform; colleges would then make assumptions about students from similar backgrounds who enroll and then refer them to mentoring sessions or more time with advisers. “You’re insulting and-or stereotyping that student,” Milliron said. Worse, colleges may feel motivated to either exclude those students from their admissions or lower their standards for issuing degrees.
Rather than focusing exclusively on race or family income, a more precise predictor of success is whether or not a student’s financial aid is adequate to address her financial needs. Students stressing over holes in their finances are at greater risk of leaving college—as Temple University learned when it turned to data to boost its graduation rates. (In fact, hundreds of schools are offering emergency small loans and grants to students who may be at risk of dropping out due to diminished funds.) Another telltale sign that some students may be off track is whether they have enrolled in a key requisite to their major by a certain point early in their college tenure. Sending alerts to them or their advisors can preempt the cascading effects of taking the necessary classes too late.
Maintaining a staff of data analysts who are able to monitor student behavior in real time across multiple variables can be expensive for colleges, but the payoffs can be huge. Corey of Arizona State University said since his school began using predictive-analytics programs nearly a decade ago, it’s seen its graduation rate climb by 20 percent. One tool ASU has relied on is College Scheduler, a product that several hundred postsecondary institutions have used. Before they sign up for classes, students enter personal information into a dashboard program that spits out possible course schedules and take into consideration their personal and academic obligations, like being a working parent pursuing biology who has to pick up a daughter from daycare. The tool can be valuable because many students may otherwise end up taking courses that don’t count toward their major, wasting their time and financial aid. At ASU, the College Scheduler auto-populates with the courses students have to take, Corey said.
“Students, unless they’re John Nash, Jr., can never do that matching,” said Milliron, who added that College Scheduler has been shown to boost college-completion rates by more than three percent.
Still, all that data requires a high degree of training and security, because universities have “data points on a student encompassing almost every single aspect of that student’s life in a way that no one else does,” said Brenda Leong, a senior counsel and director of operations at the Future of Privacy Forum. Beyond the abuses of power that are potential hazards with the use of predictive analytics, there’s also the difficulty of ensuring a student’s privacy. Leong noted that just by knowing someone’s birth date, gender, and zip code, there’s an 87 percent chance she could determine that person’s identity. Leong said she often hears boosters of big data referring to the growing amounts of student information as “fields of gold.” “That’s the kind of phrase that puts a lot of people off,” she said. “It’s not data, it’s students; it’s real people with real lives.”