May 31 2019

Universities Use AI to Boost Student Graduation Rates

Higher education faculty use machine learning and data analytics to extend their reach for student support.

Higher education institutions are using machine learning platforms to help struggling students achieve academic success. 

Digital transformation is a high priority among colleges, especially to target student need. 

The partnership of data analytics and automation helps universities such as the University of Florida keep track of their students’ academic processes to ensure those falling behind get the support they need to successfully complete their education. 

“What this does is give these support personnel who play an important role in the life of all students — and particularly those students who are needing some direction — more information,” Andy McCollough, associate provost for teaching and technology and a professor of finance, told EdTech. “It keeps them up to date so they can be proactive in helping these students achieve their academic goals and then directing them in ways that will be consistent with their success.” 


Community Colleges Find AI to Be Instrumental for Student Success

Ivy Tech Community College leveraged machine learning to identify and offer support to students at risk of failing courses, says Brendan Aldrich, former chief data officer at the Indianapolis institution and now the chief data officer at California State University.

IT first asked members of the academic affairs and student success departments what behaviors characterize strong students and what advice they’d give to students lacking those behaviors. They then identified data that related or correlated to those actions and built them into a machine learning model.

In the first term Ivy Tech used this technology, more than 800 faculty and staff called the students identified as at risk of failing to offer very specific pieces of advice, such as the time and location of free math tutoring, based on the needs of each student. 

Of 60,000 students enrolled across the state, the program identified 16,247 at risk of failing courses. By midterm, the college’s failure rate dropped year over year by 3.3 percentage points — meaning that more than 3,100 additional students were passing their classes than students at the same time the previous year.

To learn more about how universities are using automation to improve student education, check out "Digital Transformation: Opportunities for a Better Student Experience."

Rattankun Thongbun/Getty Images

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