Banks use credit scores to predict whether you will pay back that loan, baseball teams use advanced analytics to determine whether a player is worth that mega-millions contract, Google and Facebook use algorithms to predict what ads you might click on. This got me thinking about student debt and the conundrum that students face in their ability to predict the future; especially will they graduate (which will improve their earning ability) and find a good-paying job. With all the talk of Big Data, I thought someone must have tried to solve this problem. I remember student lenders touted their ability to do this back in the boom days of 2006-07, unfortunately they didn’t survive long enough due to to see if their predictions were accurate.
This article from VOX caught my attention as it showed how Big Data is coming to colleges looking to bolster their graduation rates (less than 6 in 10 students who enroll as freshman graduate within 6 years as this chart below indicates):
Colleges use this data for primarily two purposes: identify at-risk students and to nudge students toward overcoming hurdles to graduate within 4 years.
College administrators see two ways they can harness the power of Big Data to eventually help students. The first plunges into the heart of the college completion crisis to identify the students who are at risk of not graduating at all, as at UT-Austin. Often these are students who face odds outside their control: they are from underrepresented minorities, or from poor families, or are starting college later in life. With those students identified, colleges want to build them a support system to see them through to graduation.
The other strategy focuses on a more insidious contributing factor to both dropout rates and soaring student debt: for too many students, college takes longer than four years. Sometimes this is because of life circumstances for students who need to work or raise a family. But other delays are more preventable: students might not be taking enough credits to graduate in four years. Or they can’t get into classes they need because campus is too crowded. Or they change majors too late and have to add a semester or two for newly required classes.
Of course, there is a delicate balance here given that the future for 18 year olds is not set in stone. It will be interesting to see how these models evolve over time and whether students will have access to these models as they consider the amount of debt they will take on to complete their education:
In telling the future, colleges must strike a delicate balance. They need students to believe that the trends they’re projecting are real — real enough to get them to seek out tutoring help, or try a major where students with similar track records have succeeded. They need the grantmakers and administrators who hold the purse strings for support programs to believe that pinpointing the students less likely to succeed really will help them graduate.
But even as they bow to the power of Big Data to predict the future, colleges need even more to believe that future is not set in stone.
“For a lot of people, knowing there’s a problem and not being able to do anything about it — it’s almost as if we’re failing on the job,” says Wagner, who cautions against seeing her database of 1.8 million students as a “magic 8-ball” of higher education. “The idea of knowing what’s going on is really important. But knowing what you can do to address that is probably even more important.”