Technology to reach new heights. To delve deep, the University of Arizona is tracking freshman students’ ID card swipes to detect the students which are more likely to drop out. In all hopes, University researchers want to use this data to lower dropout rates i.e. referring to students who have left higher education entirely and those who want to get transferred to other colleges.

This research i.e. the card data states that how frequently a student has entered a residence hall, library, and the student recreation center, which includes a salon, convenience store, mail room, and movie theater. These cards are also meant for buying vending machine snacks and more, putting the total number of locations near 700. Also, there’s a sensor embedded in the CatCard student IDs, provided to every student attending the university.

Sudha Ram, a professor of management information systems who directs the initiative, regarded in a recent press release that, “By getting their digital traces, you can explore their patterns of movement, behavior, and interactions, and that tells you a great deal about them”.

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In fact, researchers have also gathered freshman data over a three-year time frame so far, and they found that their predictions are almost 73% accurate regarding the ones, who are likely to drop out. They also have planned for the provision of academic advisers on an online dashboard to look at student data in real time.

Now, with this data from students’ activity, academic performance, and financial aid, the university creates lists every quarter of freshman students most likely to drop out and shares it with its staff. For instance, here Ram added that those who are more likely to drop out might have shrinking social circles and a lack of fairly established patterns of behavior. The hope is that the university would pinpoint which students need more support from advisers to stay on.

To this, Angela Baldasare, assistant provost for institutional research at the university, added that “As early as the first day of classes, even for freshmen, this predictive analytics are creating highly accurate indicators that inform what we do to support students in our programs and practice”.

Ram compares the predictions to Amazon’s machine learning endeavors, adding that, “We think by doing these interventions by the 12th week, which is when students make up their mind, you’re sort of doing what Amazon does—delivering items you didn’t order but will be ordering in the future.”

In fact, after Universities, certain schools have already started using student ID cards to monitor student activity, but it could be argued that this level of analyzing students’ social interaction data, which includes timestamps and locations, potentially violates students’ privacy. No matter what, on the CatCard policy site, there’s no disclosure that swipes and payments can be monitored by the university.

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Of course, the algorithms are not always accurate and might be biased as well. So, Ram concluded that, analyzing the data as just a signal and adding to it that, “We live in an era where you shouldn’t be generalizing about ‘groups of people. You should be personalizing solutions at the individual level”.