Colleges are using big data to track students in an effort to boost graduation rates, but it comes at a cost

Iris Palmer is quoted in Hechinger Report on predictive analytics.

It wasn’t always this way. The dropout problem got a lot worse in the 1990s when more people started attending college. Young adults who used to go straight from high school to the factory floor were suddenly on college campuses.

“There was a focus on getting students into college but not a focus on getting them through college,” said Iris Palmer of the New America, a left-of-center think tank.

“There is historic bias in higher education, in all of our society,” said Iris Palmer of the New America Foundation. “If we use that past data to predict how students are going to perform in the future, could we be baking some of that bias in?”

Palmer says that the algorithms can unintentionally target black and Latino students because they’re hunting for patterns of dropping out of college, such as low grades and missed assignments. Black and Latino students might have more of these dings in their records than white students. And they could disproportionately be flagged as high risk.

“And so what will happen is they’ll get discouraged, like why are they even trying?” she said. “What that could end up doing is being a self-fulfilling prophecy for those particular students.”