You could say it was bold. You could say it was crazy; maybe even arrogant. But I decided that if Georgia State was going to do something really big, this was the goal whose achievement would allow us to change the world.”
Mark Becker, president of Georgia State University, is reflecting on his decision, soon after taking up the position in 2009, to reword and prioritise an objective in the University’s mission statement about student retention and graduation rates.
At this point, he had absolutely no idea how he was going to achieve the objective, which stated that the University would “become a national model for undergraduate education by demonstrating that students from all backgrounds can achieve academic and career success at high rates”. Yet, within three years, Georgia State’s black, Hispanic, low-income and first-generation students were graduating at the same rate as the student body overall: a first in US higher education.
Becker achieved this major milestone thanks to the power of data analytics – or big data, as it is often termed. This involves the collection and analysis of large datasets to reveal patterns and trends. And, in universities, the approach is increasingly being used to support students, manage staff effectively and make strategic management decisions.
The US has long had a retention issue in higher education, with the high cost of courses and low levels of student preparedness being key factors. One undergraduate in three on four-year degrees does not graduate within six years, according to the US National Center for Education Statistics.
But for some groups, such as non-white students, first-generation students and those from low-income backgrounds, the statistics are even worse. For instance, only about four in 10 black students graduate within six years. Graduation rates at US institutions are therefore largely a function of the characteristics of the students they admit, with the best-performing institutions typically taking higher proportions of applicants from traditional, wealthy backgrounds.
Georgia State is a large research university but its student body is not typical of such institutions since more than half come from low-income backgrounds. “The US data for those students is appalling, and we set out to show that it didn’t need to be,” says Becker.
On investigating, Becker’s team, led by vice-provost Timothy Renick, found that too many students at Georgia State were collecting credits but not getting closer to graduation. What no one had previously spotted was that students often chose the wrong classes for their intended major, or took advanced classes too soon, obliging them to waste time and money taking extra classes to catch up or repeating those that they had failed the first time around.
The reason, the team concluded, was lack of appropriate advice – and addressing that issue struck Becker as “low-hanging fruit” on his quest to improve graduation rates. “It was then that we made the commitment to big data and the commitment to having one university-wide [student] advising corps,” he says.
This realisation coincided with the arrival of new data analytics technologies in education. And, in 2011, Georgia State became one of the first universities to bring in software vendor the Education Advisory Board. The company spent weeks on its campus, building a system that would track the thousands of decisions that students make every day and predict the likelihood of their academic success on that basis.
The company used 10 years’ worth of data on every student, grade and course at Georgia State to develop and calibrate the model, which was up and running by August 2012. Every night, the system checks more than 800 variables relating to the institution’s 30,000 undergraduates to flag up those who, according to the model, are heading for trouble.
The data include which courses students have signed up for, and their attendance records and grades.
