Universities Can Predict When Students Are About to Drop Out

Universities Can Predict When Students Are About to Drop Out

Universities Can Predict When Students Are About to Drop Out

A college degree may be the golden ticket to a better job, but that incentive alone isn’t enough to stop millions of students from dropping out of school. In fact, just over half of students complete their postsecondary degrees within six years. But a lack of academic preparation is not necessarily the saboteur of their success: More than 40 percent of dropouts left their studies with at least a B average, a recent analysis of 55 colleges showed. Faced with bleak statistics such as these—in addition to scrutiny over their affordability—colleges are looking in the mirror to examine how they might do more for these students who have the talent to make it but ultimately don’t. At a growing number of schools in states like Maryland and Tennessee, the results of this soul-searching are starting to take shape as a series of digital columns and rows in spreadsheets. This reform practice even has a flashy name: predictive analytics.

More From The Education Writers Association A Push for More Latino College Graduates in Texas, but Not by ‘Business as Usual’ With More Freedom, Will States Raise Bar for ‘Effective’ Teaching? Will Californians Vote to Overturn Ban on Bilingual Education? Colleges have looked at student data before, but often “it’s data too late,” said Frederick Corey, the vice provost of undergraduate education at Arizona State University, who spoke at a meeting for education reporters last month. Corey is one of the university’s main drivers of using data to improve the students’ academic experiences. While we may view lists of numbers as the arch expression of a campus’s impersonal attitude toward its students, predictive-analytics evangelists believe that data collected the right way ultimately can personalize a student’s time at a large school in ways that weren’t previously possible. The result is a system of timely suggestions that prompts students to perform the tasks that are shown to improve their chances of completing a course, and ultimately a degree. But while the potential is high, the risks are salient, too. When does a digital nudge turn into a dictum that prevents a student from chasing her dreams? And does that digital profile become a risk to the student’s privacy? Institutions of higher learning have always gathered copious amounts of information about their students, from how many of them complete certain courses to how accurately a grade in one course predicts their success in harder classes down the line. But until recently, much of that information had been collected merely for accountability purposes—data that are shared, for example, with state and federal agencies that track how successful colleges are at graduating their students or giving them educations that allow them to earn living wages in the workplace. (It’s these data mandates that allow journalists to report on the previous year’s graduating class.)

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But what about using these data to look ahead? “I just want to understand, why is the world of education obsessed with autopsy data?” Mark Milliron, the co-founder of education predictive analytics firm Civitas Learning, recalled his partner at work asking after a few months on the job. “It’s always studying data of students who are no longer [enrolled].” And often the data are sliced to show how the average student behaves, painting a picture of the typical student that actually applies to no one. “How many of us know people with 2.3 kids?” Milliron quipped. And not all data are digital. Small colleges already have a type of predictive analytics built into its system, explained Milliron. Thanks to small faculty-to-student ratios, professors and administrators are able to make quick judgment calls about their students’ weaknesses or points of trouble—lack of participation in class, fear of making eye contact, the tremors in the voice hiding the embarrassment of being overwhelmed—and act on those observations.

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