Three years out of high school, the memories of frantically applying to colleges still gives me the shivers. Attempting to balance over 20 application essays with the added pressure of graduating high school was no easy task. Nonetheless, even if things seemed to get out of hand, I still felt that getting an acceptance was dependent on factors under my control. However, I was surprised to find out that some college admissions teams are now ranking students on a 0-10 scale based on circumstances like race and ethnicity, the high school you attended and zip code.
As a prospective student, you expect to be judged by your grades, essay and SAT scores. However, the fact colleges rate you on a scale of demographics is borderline discrimination. It’s already taxing enough to keep your grades up and avoid writing a cliché essay, but getting rejected from your dream school because of your zip code seems absurd.
Now, big data is all the rage; it makes sense that colleges are using predictive analytics to further simplify
the decision-making process and decide whether or not a student should be accepted into a college. Understandably, colleges want to predict the success of a candidate once they get accepted. In doing so, however, these colleges are exposing themselves to various biases. For example, a student could get rejected because they belong to a low income household or are part of a minority group.
Predictive analytics isn’t inherently evil, though. Research conducted by Washburn University revealed that students living on campus were less likely to drop out of college compared to students living off campus. Seeing the results, the authorities at the college decided to explore opportunities to expand on campus housing. Predictive analytics proves to be more benevolent post-acceptance. Students who delay the process of filling out their FAFSA form or who plan on taking fewer classes may signal a future decision to drop out. If colleges can gauge this knowledge prior to students actually dropping out, they can effectively manage financial aid and scholarship funds to incoming students who may deserve it more.
Ultimately, there is no way of telling how the data you put on the Internet or on your application will end up being used. It’s difficult to comprehend that rudimentary details such as your address or anticipated major could largely affect your ability to get into a college. One can only hope that predictive analytics are used to benefit students rather than put them at a disadvantage due to the biases brought with it.