Just as companies pay for consumer data to make informed decisions, it turns out, colleges and universities do the same, according to a report by non-partisan think tank New America.
The report, called “The Promise and Peril of Predictive Analytics in Higher Education,” detailed the ways in which colleges pay for student data. For less than 50 cents a name, colleges glean student data from third-party groups.
The College Board, which administers the SAT, the ACT, and the National Research Center for College and University Admissions (NRCCUA) all collect student information that schools pay for. All three are non-profits.
The students’ demographic information is then used for “predictive analytics,” a little-known x-factor that colleges often use for enrollment management. The process pulls a multitude of data points into a model that predicts the probability a particular student will apply to a school, choose to attend after they have been accepted, or perform well once enrolled.
The third-parties also have their own predictive models that colleges can pay for, which can include around 300 different data points on students.
The report also explained how colleges rank students based on this data. Admissions teams individually score students’ likelihood of becoming an applicant, being admitted, and deciding to enroll, usually on a scale of 0-10 based on factors like: race and ethnicity, zip code, high school, and anticipated major, according to the authors.
While it’s hard to put an exact figure on just how much money the College Board, ACT, and NRCCUA make from selling student data each year, Manuela Ekowo, a co-author of the New America paper, estimates the payoff is high.
“I can only imagine that they are reaping in huge sums per year because they almost have a monopoly in this space,” Ekowo told Business Insider.
School use this information for essential decisions around granting admission and provided financial aid to a particular student, according to Ekowo.
Colleges find the information helpful, but predictive analytics raises questions about discrimination. While the implicit connection between race, income, and college attendance is widely known, the predictive analytic models that Ekowo studied offers a linear determination of how these factors affect success, and thus, admission to college.
Students of colour, for example, are more likely to attend a school with a high percentage of students living below the poverty line. The schools tend to have fewer resources, which can affect the quality of education.
Predictive analytics, however, take statistics like that a step further by assigning a numeric value to predict success based on uncontrollable demographics factors — unlike scoring well on exams of having an impressive GPA.
“There’s nothing they can do about being born in a low income neighbourhood or zip code, but those are typically standard questions on any application,” Ekowo said.
But predictive analytics has its upside too. To some extent, understanding what types of student will succeed at a college is not only necessary, its laudable. Riddling students who may drop out with student loans isn’t good practice for colleges — or prospective students.
The College Board, ACT and NRCCUA did not immediately respond to a request for comment.
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