DECEMBER 8, 2012
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Ever since conservative courts and voters began trying to eliminate affirmative action in the 1990s, universities have sought creative ways to boost their enrollment of minority students without explicitly relying on race. When California voters banned racial preferences in public universities in 1996, for example, the University of California responded by adopting admissions preferences based on socioeconomic status instead. And after a federal appellate court struck down the University of Texas’s race-based affirmative action program, the school adopted a plan that guaranteed admission to those students graduating in the top 10 percent of their high school class.
When the Texas effort—known as the Top Ten Percent Plan—failed to generate the racial diversity school officials sought, the university returned to using explicit racial preferences. Those preferences are now being challenged in the Supreme Court case of Fisher v. Texas, and many expect the conservative justices to deal what could be a fatal blow to race-based affirmative action at American public universities. Once again, however, the universities have a secret weapon they hope will allow them to circumvent such a ruling: data mining.
Whether it’s used in airport security or online advertising or education, data mining works by finding patterns and correlations. Based on census data, the spending patterns of my neighbors, and my Washington, D.C., ZIP code 20016, the Nielsen Company classifies me as someone who lives among the “Young Digerati”—that is, high-income consumers who are “tech-savvy and live in fashionable neighborhoods on the urban fringe.” My fellow Washingtonians a few miles to the southeast in Anacostia are categorized using very different terms. They are the “Big City Blues,” a community of “low-income Asian and African-American households occupying older inner-city apartments.” Based on where we live and what we spend, Nielsen creates aggregate predictions about our likely buying habits so that advertisers can send us ads that reflect our interests. That’s a little creepy—but then again, we’re talking about advertising. To some education experts, however, data mining also represents the future of public education.
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, which was originally developed by the College Board to help admissions officers more efficiently target likely students. The program employs the same kinds of algorithms that Nielsen uses to provide consumer data to advertisers based on demographic patterns and spending habits, but in this case, it sorts those data into categories that are useful for higher-education institutions. Descriptor Plus works by dividing the country into 180,000 geographic neighborhoods, and then regrouping those neighborhoods into 30 more manageable “clusters” whose residents share similar socioeconomic, educational, and racial characteristics.
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.
If the Supreme Court’s decision in Fisher puts new restrictions on racial preferences, it is likely that universities will expand their use of data mining to get around the ruling. But data mining has proved to be an even less effective a way of promoting racial diversity in the classroom than the explicit preferences it’s designed to replace. 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. Sanders told me that the minority students admitted under Descriptor Plus are, by definition, less academically qualified than those admitted under the Texas' Top Ten Percent Plan—because if they had graduated in the top 10 percent of their class, they would have gained automatic admission without the Descriptor Plus boost. By admitting minority students with lower levels of academic preparation than those admitted under the Top Ten Percent Plan, Sanders said, programs like Descriptor Plus might exacerbate the problem of racial mismatch and self-segregation.
WHILE LEGAL PRESSURES on affirmative action prompted the initial expansion of data mining as an admissions strategy, schools are also beginning to use it for other purposes—and in ways that may result in ever more segmentation and segregation of students based on their racial backgrounds, tastes, and preferences.
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. Although the program also records students’ race and ethnicity, Denley said he found a disproportionate grade swing in students from lower socioeconomic groups, but not from minority groups in particular.
Another program his university uses, My Future, employs similar predictive analytics to recommend majors in which students are most likely to get good grades and graduate on time. “Students are less likely to choose sociology as an incoming major,” says Denley, “because people don’t do sociology in high school; instead, lots of students choose business, pre-law or pre-med.” He hopes that by exposing students to a broader range of majors they may not have considered, My Future will help to match them with fields and careers in which they’re likely to thrive.
As college and even public high school and elementary schools record the race of students as part of their data-mining programs, there’s likely to be increased pressure to steer students with similar backgrounds into similar classes, reducing diversity in the classroom as a whole. Public high schools and even some elementary schools are beginning to input information about students’ race and ethnicity in giant databases that track their academic performance in order to construct models about what kinds of students are most likely to succeed in particular classes.
Highland Park Elementary School in Pueblo, Colorado, recently adopted a data mining program called Infinite Campus that is operated by Pearson, the textbook publishing giant. Ronda Gettel, who coordinates math and English programs at Highland Park, and she tells me she was shocked when her supervisors asked her to input information about the ethnicity of individual students while grading a math and reading program. “I was putting in how they self-reported their ethnic background, whether they’re black or Hispanic, and whether they’re getting free or reduced lunches, and their socioeconomic patterns,” says Gettel. “I thought maybe we shouldn’t be doing this—I’m a person that’s against tracking.”
Of course, guidance counselors have always had the power to steer students toward classes that coincide with their interests and ability levels. But Gettel and others are concerned that by slicing and dicing students into profiles and clusters, data mining threatens to segregate classrooms in more permanent ways, creating profiles from which students can’t easily escape, and placing minority students into less rigorous classes because of the predictions of computer programs.
Diversity in the classroom is valuable because it encourages students to interact with peers from very different backgrounds and to explore classes and careers that might not have occurred to them before they enrolled. But not all human choices can be predicted by algorithm. If the Supreme Court eliminates the use of race-based affirmative action, and drives schools to pursue an ersatz diversity through profiles and computers models, it may inadvertently encourage the proliferation of technologies that allow even less consideration of students as individuals than the racial preferences they’re designed to avoid.
12 comments
These data-mining excresences are going too far. I entered UofM with the intention of studying Chemistry. As the first two years involved courses in different topics Math, History, Chemisty, Philosophy, English, a mandatory 2nd language German as my Chemisty teacher had suggested, I ended up with English Major and German Minor, was given a Scholarship abroad after four years to continue German etc.... the rest is history (oh, and fulfillment and happiness.) College or University should be a place that both demands certain topics, allows choices, and is open to people changing directions. I went to univeristy to learn what I wanted to become, not with a particular job in mind. Okay, I'm old, those days are gone, but what does future mean to your kids now? Severely out of touch,Ken
- kras
December 8, 2012 at 7:12am
Nielsen may now own Claritas, but Claritas socio-demographic segments by zip code have long been used for retail store locations and political contests, not just advertising. I used Claritas to house hunt in 2000; very helpful. As for affirmative action for college admission? It fails to address the dilemma of poor primary education, which handicaps anyone regardless of skin color.
- K2K
December 8, 2012 at 7:53am
This narrowing of choice is happening in law school, as well. Law schools are changing the curricula so that students pick their specialties after one or two years, and then focus on the specialties, either in the classroom or in clinics and other applied settings. I think it's a big mistake. I didn't pick my specialty until well after finishing law school and was better prepared for it because of the diversity of subjects and experiences. If I had narrowed by choice to criminal law after two years, I would not have had the same satisfying and rewarding career. If law students, age 25 or older, don't know what they will ultimately choose as their specialty, how could an undergraduate, age 18 or 19. If my background had been plugged in some data mining software, would it have directed me to my specialty? Does anybody actually believe that, other than the creators of the software.
- rayward
December 8, 2012 at 10:28am
The tease asks "What will replace affirmative action if the supreme court kills it?" Our goal as a society should be to help those that can benefit society get to college if money is an obstacle. A smart kid with good grades/test scores (top 5%) but no money deserves college. Period. A kid in the top 10% deserves lots of help. A kid in the top 15% could hopefully pay for college via a part-time job and a grant here and there. A kid that is in the middle of the pack doesn't deserve much financial aid, or even a preferential consideration regardless of skin color. From the article: "When the Texas effort—known as the Top Ten Percent Plan—failed to generate the racial diversity school officials sought, the university returned to using explicit racial preferences. " You deceive the reader here. What UT wanted from their diversity was more PRIVILEGED blacks and Hispanics. The UT program admitted plenty of underprivileged minorities through its top 10% policy. The top 10% of black kids at a black high school got in to an exceptional university at a very cheap price. That is awesome. What UT felt was missing was black kids from upper middle classes in predominately white schools. The justices specifically asked if the professional parents of a black child who were in the top 1% of earners deserve a leg up over an Asian or white child whose parents are of modest education and income. From the article: "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" Many kids SAY they want to be an engineer. They have no clue what it involves. When they understand their first 10 years will be spent soldering and measuring while knee deep in tedium, or that their first 10 years will be spent cranking code among dorks, their answer changes. At my university, the graduation rate was less than 40% of incoming freshman, and the incoming freshman were very well qualified to do the work. An engineering degree is really hard for a reason. Yes, you can dumb down the degree (which some computer science degrees are doing), but then whatever results isn't as valuable. And make no mistake: The ST and M in STEM are not high paying jobs. They are OK paying jobs. But engineering is a high paying job because there are so few that can complete the program. Sending a kid into an engineering program without the right skills is sending a lamb to slaughter. Trust me, I know this first hand. Our goal as a society should be to recognize the brightest at all of our schools and get them to college. Note that this means the top 10% of a predominately black inner-city school are just as important as the top 10% at a white private school. The Texas policy did very well in that regard. It should be held up as a model for the rest of the country. To argue as they did that Will Smith's kid is an important part of diversity and thus his kid should be given preference over a poor kid with higher scores from Appalachia is disgusting.
- seattleeng
December 8, 2012 at 3:47pm
You write: "...African Americans pursuing STEM majors were more than half as likely as whites to finish with a STEM degree". You probably mean "less than half as likely". Or maybe not; it's not clear what you mean. One of my grade school teachers, Eleanor Wilson Orr went on to teach math in high school, many of her students were Black; she concluded that one of the causes for the difficulty they had with math is that Black English lacks certain kinds of language to describe quantity. She wrote a book about this and what to do about it, titled Twice as Less because some of her students would say "twice as less" where she would say "half as much". Maybe Mr. Rosen should take a look at it.
- jonrysh
December 8, 2012 at 7:40pm
I just finished "Mismatch", Sander and Taylor, and highly recommend it. The data and analytical material is convincing but the anecdotes concerning the treatment of those who question the diversity orthodoxy are also fascinating. Much of what the academic establishment might have learned from the CA Prop 209 experience and other "natural experiments" has been ignored or forgotten.
- nehocm002
December 8, 2012 at 8:06pm
I heard on NPR that the human genome has been mapped, and parents are deciding whether to analyze their children's genetics even before they are born. Philosophers are now arguing that humans have no free will. I can imagine that upon birth children of the future will be presented with a blue print declaring: "This is your life. You have no choice in the matter. Signed, Aldous Huxley. Of course, is all this data mining reveals a future serial killer, the child will be aborted.
- skahn
December 8, 2012 at 11:20pm
Just ordered mismatch based on the recommendation above. Looks very interesting. Somewhere I read a point that is applicable here: Owning a house and car are byproducts of being middle class, not the price of admission to being middle class. The confusion here is important, because a government that believes if you have a house and car then you are middle class will rig the system to make sure you can get a house and car. But you don't borrow your way into the middle class. Instead, you produce your way into the middle class. In other words, you deliver a level of productivity to society that in turn permits you to afford a house and car. Similarly, college has historically been geared for roughly the top 20% of the population and exists to groom future leaders across all disciplines. If we aim to get 50% of the population through college, then that can either happen by raising the collective smarts of the next 30% of the population OR by dumbing-down college. Since we already outspend the entire world on education and the results are getting worse by the year, the former won't happen. And instead, we are in the midst of dumbing-down college. But we dumb down college at our own peril. If the Harvards of the world dumb down their curriculum to get unprepared students through, then it says we're not being as hard on our future leaders as we should be. Students used to hit their freshman year ready to dive into calculus and write lofty 30 page papers to simple open-ended questions about a major piece of literature. Anymore, they paying $20K/year to be spoon fed algebra 2 and take remedial writing courses. The first 2 years of college is very expensive remedial work, followed by a 2 years of some modest specialization, followed by graduating into a world that doesn't really need the skills you have acquired. And by the way, you have $80K in debt that the government and universities told you was worth taking on. Not a good deal for society. It doesn't help the average kids, and it doesn't help the top 10% kids either. I read some place too that the liberal arts degree that is so widely derided today used to be hard as hell. And the book went through examples of just how difficult it was to get a degree in liberal arts. That is because the program historically was to prepare society for the next wave of leaders. We can pooh pooh how good those leaders were, I guess, but the collective track record of the west during the golden age (perhaps 1850 to 1950 or so) speaks for itself. At some point, the liberal arts degree was dumbed down to the point it became a joke. Liberal arts, journalism and communication degrees seemed to be the punchlines when I was in school. Maybe it's changed.
- seattleeng
December 8, 2012 at 11:48pm
I'm sympathetic to affirmative action based on socio economics rather than race. I may have missed it but isn't a program so conceived an excluded middle here from race based affirmative action, however it's packaged, at one binary end and the extremes of data mining at the other. I went to a Canadian law school too many years ago and have not kept pace with changes in legal education in Canada, let alone the U.S. But what if what Rayward describes is so, that trend seems self evidently absurd, a kind of reductio ad absurdum of data miining. We of necessity in our first year at least had to take the usual suspects of courses that comprise the substance of black lette law. But even way back then we could completely fill in our second and third years with any offered courses we liked, no matter how remote from the law of day in day out legal practice. My view is that back then there was too much liberality in that and that more mandatory courses would have been better for reasons including those cited by Rayward. To bring this back to the idea of date mining: I'm sympathetic to students being generally steered towards where their aptitudes lie as long as that's done in concert with providing some measure, if it's in liberal arts, of a general humanities education. Students then can have the possibility described by krasumussen of sampling the studies their aptitudes seem to point and decide whether that's the academic path for them. Professional schools are horses of other colours, and data mining in them, if what Rayward describes is correct, implicating students in not getting a grounding in the staples of of their vocation, letting alone steering them into areas of specialization right out the chute, is altogether a bad thing. Mind you, on reflection, I have some trouble understanding how this steering would work, at least forma legal education. Unlike the intellectual spread between STEM and arts, the underlying analytical skills and aptitudes for legal studies is generally of a certain order regardless of subject matter and doesn't track the aptitudes bending towards STEM as different from those bending towards the humanities. A big divide amongst lawyers is litigating as against solicitors work. But that divide seems more a matter of temperament and propensities remote from the underlying analytical abilities generally comprising good lawyers.
- basman
December 9, 2012 at 2:59pm
Discrimination of white against black is wrong. Discrimination of black against white is OK. This is why the SCOTUS will kill the stupid idea of affirmative action. What will replace it? Nothing....................
- perrym
December 9, 2012 at 11:45pm
Another excellent survey. If your previous article correctly advocated transparency in the process for & outcomes of diverse admissions & retention in higher education, then this one correctly critiques the over-determination of student potential based on algorithms using historical data. It is circular to discourage underrepresented students from certain fields based on projections from historical data, during which time that population was excluded from the data set, overtly or otherwise. A classic example -- but not the only one -- is law: For too long, women were actively kept out of this field, so a career suggestion based on historical data about the success of female lawyers would be improperly skewed towards discouragement, at least prior to about 1970 in the US. Similar arguments may be made for medicine (now graduating more women than men in the US) and, more recently, even physics. If the point of affirmative action is to rectify the omission of underrepresented groups in student populations, both in the main and in sub-populations such as lawyers, then taking into account the potential of these populations to exceed their historical record must be an explicit principle of successful programs, and implicit in their underlying admission formulas. Garbage in, garbage out.
- Wonderland
December 10, 2012 at 5:09pm
Wonderland, tight comment. A few questions: Why "over-determined"? Your use of this word seems to undermine the argument moving through Rosen's piece, which might be encapsulated as "under-determined." I don't know enough about the structure of the " guidance" emerging from the mined data, but is women in law really a good analogue? For that there were social conventions having to do with women's and men's proper social roles and that generally neither took into account individual talent nor utilized predictors of individual success like aptitude tests such as SATs and the LSATs. I can't imagine any counsellor discouraging a kid from a certain socio economic slice who has high marks in fields related to what he or she wants to study and or in the relevant aptitude tests. I think your point conflates "discourage" and "presupposition" where that conflation isn't warranted. In any event I agree with the point in your last paragraph but I don't sense that data mining is such a hindrance to that desired principle as you suggest.
- basman
December 10, 2012 at 5:47pm