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The Mathbabe Finds a Constructive Application of Big Data

November 1, 2016

In a blogpost today, Cathy O’Neill (aka the Mathbabe) assesses an article by NPR reporter Anya Kamenetz describing a program at Georgia State University, and declares this college is using Big Data to provide a Big Boost to struggling college students. The article by Kamenetz describes how Georgia State built an “early warning system” to identify students at risk of failure based on the analysis of 2.5 million grades assigned over a ten year period. This so-called GPS system (for Graduation Progression Success) issues daily reports to academic advisors to help them know when intervention is needed and likely to be effective.

Several Mathbabe readers alerted her to this algorithmic method of monitoring students using Big Data expecting that she would find it wanting… but she rated it as a qualified success. Her assessment:

wrote a recent book about powerful, secret, destructive algorithms that I called WMD’s, short for Weapons of Math Destruction. And naturally, a bunch of people have written to me asking if I thought the algorithm from this article would qualify as a WMD.

In a word, no.

Here’s the thing. One of the hallmark characteristics of a WMD is that it punishes the poor, the unlucky, the sick, or the marginalized. This algorithm does the opposite – it offers them help.

Now, I’m not saying it’s perfect. There could easily be flaws in this model, and some people are not being offered help who really need it. That can be seen as a kind of injustice, if others are receiving that help. But that’s the worst case scenario, and it’s not exactly tragic, and it’s a mistake that might well be caught if the algorithm is trained over time and modified to new data.

According to the article, the new algorithmic advising system has resulted in quite a few pieces of really good news:

  • Graduation rates are up 6 percentage points since 2013
  • Graduates are getting that degree an average half a semester sooner than before, saving an estimated $12 million in tuition.
  • Low-income, first-generation and minority students have closed the graduation rate gap.
  • And those same students are succeeding at higher rates in tough STEM majors.

At the end of the post, Ms. O’Neill emphasizes that algorithms per se are not evil. Rather the way the algorithms are USED is the issue… and in the case of Mount St. Mary’s College in MD (see earlier posts on the Mathbabe site as well as this one) algorithms were used for nefarious purposes. In the case of Georgia State, their intention was good and their use was effective.


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