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Predictive Analytics

Since 2010, USF has been dedicated to student success, driving up its student retention and graduation rates significantly with a variety of initiatives. By 2012, the initially identified improvements and intiatives had been implemented and progress slowed, with retention and graduation rates stalling.

To move the needle, interest developed in the use of data to predict first-year student persistence. Utilizing an internally developed model, Student Affairs professionals began identifying individual students in need of support and leveraging its staff and student employees, such as Resident Assistants, to coordinate contact with these student populations to help them progress, as well as creating targeted programming to engage them.

With success with internal modeling, USF recognized the power in data to uncover student population behaviors and trends. To expand its initiatives and boost its retention rates, the university turned to higher education thought leader, Civitas Learning, in 2014 to deploy a predictive analytics platform to generate predictors of persistence for all students.

This innovative predictive analytics modeling software analyzed real-time individual student data鈥攊ncluding grades, class participation, absenteeism, etc.鈥攆ed from both the university鈥檚 student information and learning management systems to pinpoint students facing challenges so that the university could take action to support them so they remain on a successful path.

In recent years, USF has expanded its own proprietary predictive modeling with impactful results to the point that third party services are no longer necessary.