Will You Graduate? Ask Big Data – AI predicts those who will graduate
“You could get a C or an A in that first nursing class and still be successful,” said Timothy M. Renick, the vice provost. “But if you got a low grade in your math courses, by the time you were in your junior and senior years, you were doing very poorly.”
The analysis showed that fewer than 10 percent of nursing students with a C in math graduated, compared with about 80 percent of students with at least a B+. Algebra and statistics, it seems, were providing an essential foundation for later classes in biology, microbiology, physiology and pharmacology.
A little less than half of the nation’s students graduate in four years; given two more years to get the job done, the percentage rises to only about 60 percent. That’s no small concern for families shouldering the additional tuition or student debt (an average of more than $28,000 on graduation, according to a 2016 College Board report). Students who drop out are in even worse shape. Such outcomes have led parents and politicians to demand colleges do better. Big data is one experiment in how to do that.
Different courses at different universities have proved to be predictors of success, or failure. The most significant seem to be foundational courses that prepare students for higher-level work in a particular major. Across a dozen of its clients, the data analysts Civitas Learning found that the probability of graduating dropped precipitously if students got less than an A or a B in a foundational course in their major, like management for a business major or elementary education for an education major. El Paso Community College’s nursing hot spot was a foundational biology course. Anyone who got an A had a 71 percent chance of graduating in six years; those with a B had only a 53 percent chance.
At the University of Arizona, a high grade in English comp proved to be crucial to graduation. Only 41 percent of students who got a C in freshman writing ended up with a degree, compared with 61 percent of the B students and 72 percent of A students.
“We always figured that if a student got a C, she was fine,” said Melissa Vito, a senior vice provost. “It turns out, a C in a foundation course like freshman composition can be an indicator that the student is not going to succeed.” The university now knows it needs to throw more resources at writing, specifically at those C students.
At Middle Tennessee State University, History 2020, an American history course required for most students, has been a powerful predictor. The most consistent feature for those who did not graduate was that they received a D in it. “History is a heavy reading course,” said Richard D. Sluder, vice provost for student success, “so it signifies a need for reading comprehension.”
Before predictive analytics, Dr. Sluder said, many of the D’s went unnoticed. That’s because advisers were mainly monitoring grade-point averages, not grades in specific courses. “You take a student who’s getting A’s and B’s and you see a C in this one class,” he said, “and you’d say, ‘He looks fine.’ But, really, he was at risk.”
Such insight may revolutionize the way student advising works.
One woman who was planning to major in psychology had taken it and three other courses as a freshman in the fall of 2014. She earned three A’s and a C.
“It was a pretty decent start,” Ms. Mercer said. “But guess what? The C was in Psych 1100.” In the spring of 2015, the student signed up for five classes. She withdrew from one. The next semester she withdrew from three of her five classes. This fall she took four classes and withdrew from all of them.
“It was just what the analytics had predicted,” Ms. Mercer said. “I tend to be a little skeptical. It wasn’t until I dove into the records and I saw, ‘Yes, indeed, this is a problem.’ ”