An article in The New Republic explains that the Supreme Court might be on the verge of definitively banning “affirmative action” at public universities—but it also describes the high-tech method these schools are already planning to use to subvert such a ruling.
After Michiganders voted in 2006 to ban the use of racial preferences in college admissions, the University of Michigan wasn’t willing to give up on the goal of enrolling more minority students. So it turned to a data-mining program called Descriptor Plus….
Take two distinct clusters identified by Descriptor Plus. High School Cluster 29 is most likely to include high-achieving students who have aced standardized tests, stand out in their elite private high schools, and demonstrate superior math ability. “There is very little diversity in this cluster,” notes Descriptor Plus. By contrast, the students in High School Cluster 30 are much more likely to be ethnically diverse. While also college bound, they have far fewer resources than the junior achievers in Cluster 29. “These students,” concludes Descriptor Plus, “will typically end up at a local community college.”
Armed with the Descriptor Plus categories, the University of Michigan could give preference to applicants from low-income clusters like 29, in which African-American students were disproportionately represented, without explicitly relying on race. The method worked. Two years after Michigan voters banned the use of racial preferences, Michigan’s freshman class saw a 12 percent increase in African-American enrollment, even as the overall class size shrank and other minority groups lost ground.
But the essential evil of “affirmative action” was never really about race. It was about giving the unearned, elevating some applicants to spots at a relatively elite institution because they had not earned it by individual merit. Which turns out not to be good even for the supposed beneficiaries.
In a new book, Mismatch: How Affirmative Action Hurts Students It’s Intended to Help, and Why Universities Won’t Admit It, Richard H. Sander and Stuart Taylor, Jr. note that as seniors in high school, African Americans are more likely than whites to express interest in majoring in science, technology, engineering or math majors, known as STEM. Once admitted to elite schools, however, African Americans pursuing STEM majors were more than half as likely as whites to finish with a STEM degree: students who feel less prepared than their classmates tend to leave science for less challenging humanities courses after their freshman year.
With “data mining,” this takes a particularly creepy turn.
Tristan Denley, the provost of Austin Peay State University in Tennessee, has developed data mining programs designed to steer students toward the courses and majors in which they are most likely to succeed. One such program, Degree Compass, uses predictive analytics to estimate the grade a student is most likely to receive if he or she takes a particular class. It then recommends courses in which the student is likely to earn the highest grades. “It uses the students’ transcript data, all of their previous grades, and standardized test scores, and it combines that with the data we have with thousands of similar students who have taken the class before,” Denley told me. He said the predictions are accurate—within a half letter grade, on average. And he noted that students from lower socioeconomic backgrounds who used the program to select their classes experienced a more pronounced grade swing—from lower to higher grades—than students from higher socioeconomic groups, perhaps because they were being steered into easier courses.
The author of the New Republic piece complains that banning racial preferences will “encourage the proliferation of technologies that allow even less consideration of students as individuals than the racial preferences they’re designed to avoid.” But not considering students as individuals was the point of the whole thing in the first place, wasn’t it?
The supposed idea of affirmative action in higher education was to raise up the prospects of students from racial minorities by giving them access to the higher-quality education offered at elite institutions, and the increased value, in the job market, of a degree from such an institution. But in trying to give them a value they have not earned, it converts more of them into college dropouts or steers them into undemanding fields where degrees have little commercial value. (One of the examples cited in this article is “sociology,” which is pretty much worthless.)
This whole story is a lesson on the fraud of altruism. By seeking to give the unearned, it seeks to overturn reality—to treat an unqualified student as if he is qualified. That fraud cannot be maintained, and the result is to distort the choices of the supposed beneficiaries and divert the paths of their lives. Now, thanks to “data mining,” that process will take an even more convoluted twist, as universities are encouraged to shunt their affirmative action applicants into a kind of semi-official second-tier track of undemanding classes and majors. They will still get into college, but their education will be—what’s the phrase I’m looking for?—separate and unequal.—RWT