Thursday, 22 March 2018

Big data, student dropouts, and failing fast

Amy Wang at Quartz reports:
At the University of Arizona, school officials know when students are going to drop out before they do.
The public college in Tucson has been quietly collecting data on its first-year students’ ID card swipes around campus for the last few years. The ID cards are given to every enrolled student and can be used at nearly 700 campus locations including vending machines, libraries, labs, residence halls, the student union center, and movie theaters.
They also have an embedded sensor that can be used to track geographic history whenever the card is swiped. These data are fed into an analytics system that finds “highly accurate indicators” of potential dropouts, according to a press release last week from the university. “By getting [student’s] digital traces, you can explore their patterns of movement, behavior and interactions, and that tells you a great deal about them,” Sudha Ram, a professor of management systems, and director of the program, said in the release. “It’s really not designed to track their social interactions, but you can, because you have a timestamp and location information,” Ram added...
The University of Arizona currently generates lists of likely dropouts from 800 data points, which do not yet include Ram’s research but include details like demographic information and financial aid activity. Those lists, made several times a year, are shared with college advisers so they can intervene before it’s too late. The schools says the lists are 73% accurate and Ram’s research yields 85% to 90% accuracy, though it did not give details on how those rates are measured.
This sort of story isn't anything new. I blogged on a story about the University of Maryland doing something quite similar back in 2016. Student retention is a big issue, and its often presented as such because losing students results in lost custom for universities. However, there is another aspect of students dropping out that is more than a little problematic for me as an economist.

On the one hand, as a teacher I don't like to see students' efforts go to waste. And signing up for a degree programme that you don't complete really is a waste (and you'll be paying off those student loans for a while, for little to no benefit). On the other hand, as an economist I recognise that past costs that cannot be recovered are sunk costs, and shouldn't be relevant to a student's decision today about whether they complete their degree (those past study costs have already happened - you won't get them back if you drop out, but you also won't get them back if you continue to study either). The decision about whether to continue to study should come down to a dispassionate analysis of the future costs and benefits of continuing to study, and not be affected by things that have already happened and can't be changed.

How do I reconcile those two views? If we can identify at-risk students, perhaps we can help to find ways to ensure they succeed. Or, maybe we can counsel them on alternative options that would avoid wasting future study costs. In the latter case, I'm sure there are at least some students (hopefully few) for whom university study is probably not the best option, or at least not the best option for them at this time. For instance, I have a current research project that is looking into the reasons why students (specifically management students) drop out of university, and in many cases it is non-student life intervening that makes study difficult (more on that in a future post).

In the case of these students, I would expect that the Silicon Valley mantra failing fast and failing cheap applies, even though it has come under a lot of fire of late (see here or here, for example). If a student is going to fail anyway, wouldn't it be better to fail at low cost after one or two semesters than to fail at much higher cost after many semesters of low performance? Or, in the case of university students, perhaps the mantra should be failing fast and coming back later when life is no longer getting in the way? (that was my road through university study, after all).

[HT: Marginal Revolution]

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