NY Times: “Big Data on Campus”

The New York Times, in collaboration with the Chronicle of Higher Education, has published an excellent article by Marc Parry on “Big Data on Campus: Colleges Awakening to the Opportunity of Data Mining“.

The article highlights the growing trend of colleges and universities of “advising by algorithm,” a Netflix-style approach to mining students’ past academic performance, current interests, data from similar students, and the like in order to predict what classes and majors a student might be best suited for. For example:

With 72,000 students, A.S.U. is both the country’s largest public university and a hotbed of data-driven experiments. One core effort is a degree-monitoring system that keeps tabs on how students are doing in their majors. Stray off-course and a student may have to switch fields.

And while not exactly matchmaking, Arizona State takes an interest in students’ social lives, too. Its Facebook app mines profiles to suggest friends. One classmate shares eight things in common with Ms. Allisone, who “likes” education, photography and tattoos. Researchers are even trying to figure out social ties based on anonymized data culled from swipes of ID cards around the Tempe campus.

This is college life, quantified.

I was contacted by Mr. Parry to comment on any ethical issues I see with such a reliance on algorithms to predict student performance and place them in majors. My replies are in the article, including:

“We don’t want to turn into just eHarmony,” says Michael Zimmer, assistant professor in the School of Information Studies at the University of Wisconsin, Milwaukee, where he studies ethical dimensions of new technology. “I’m worried that we’re taking both the richness and the serendipitous aspect of courses and professors and majors — and all the things that are supposed to be university life — and instead translating it into 18 variables that spit out, ‘This is your best fit. So go over here.’ ”

and

Some express concerns about deferring such important decisions to algorithms, which have already come to dictate — and limit — so much of what we see and do online. Mr. Zimmer, the Milwaukee information-studies professor, sees the value in preventing students from going down paths that may frustrate them or cause them to quit college. But as higher education gets more efficient, he fears the loss of the unanticipated discovery.

“It’s the same as if you’re worried about whether or not Google or Amazon are going to present you with alternative topics, or only the topics that fit your history,” he says. “We hope the role of a university is to make sure people are exposed to diverse things and challenged.”

These are really interesting and complex issues, and I hope this article generates the necessary contemplation and discussion related to entrusting college education to datasets and algorithms. Please, read the full article, and let me know what you think.

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