mathbabe

There’s an article that’s been forwarded to me by a bunch of people (I think first by Becky Jaffe) by Anya Kamanetz entitled How One University Used Big Data To Boost Graduation Rates.

The article centers on an algorithm being used by Georgia State University to identify students in danger of dropping out of school. Once identified, the school pairs those wobbly students with advisers to try to help them succeed. From the article:

A GPS alert doesn’t put a student on academic probation or trigger any automatic consequence. Instead, it’s the catalyst for a conversation.

The system prompted 51,000 in-person meetings between students and advisers in the past 12 months. That’s three or four times more than was happening before, when meetings were largely up to the students.

The real work was in those face-to-face encounters, as students made plans with their advisers to get extra tutoring help, take a summer class or maybe switch majors.

I 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.

But to be clear, the real “secret sauce” in this system is the extraordinary amount of advising that’s been given to the students. The algorithm just directed that work.

A final word. This algorithm, which identifies struggling students and helps them, is an example I often use in explaining that an algorithm is not inherently good or evil.

In other words, this same algorithm could be used for evil, to punish the badly off, and a similar one nearly was in the case of Mount St. Mary’s College in Virginia. I wrote about that case as well, in a post entitled The Mount St. Mary’s Story is just so terrible.