At the University of South Africa (Unisa) in Pretoria, the typical student experience could take place anywhere. As an “open distance learning” institution, students attending Unisa receive a course textbook and a study guide to direct them through course materials. If learning takes place online, students may use a learning management system (LMS) to log into their courses, and even watch live video lectures.
Unisa claims to serve nearly one third of South African students alone. But enrollment isn’t the school’s biggest challenge; rather, it’s figuring out how to best ensure students are engaged in their learning and reach their academic goals. The university has started using predictive analytics as a solution to that, but, like many colleges and universities throughout the world, is grappling with how to do so without discriminating against students based on assessments of their needs and potential, and making the correct inferences from quality data.
With students hailing from 130 countries around the globe (91 percent of students are from South Africa), part of the reason for Unisa’s astronomical student enrollment figure is due to the university’s distance education mode of instruction. Unisa is also a relatively affordable institution. Tuition and fees for the first year of a bachelor of arts degree in 2015 was R13,600 (a little over $1,000 U.S. dollars), according to Africa Check, a nonprofit that tracks information on African crime, health, education, and more.
Unisa’s teaching model has served many students well. Nelson Mandela received his bachelor of laws (LLB) from the college in 1989 while imprisoned. But, like most primarily distance education institutions, Unisa faces challenges that it must tackle to ensure its diverse student population graduates. One way to assess students’ progress and intervention needs is to use predictive analytics—decoding patterns in historical data to predict events that can impact a student’s academic journey and supporting them through in the best way possible.
In order for Unisa to use predictive analytics to improve these outcomes though, the school may first have to jump through some ethical and technical hoops. For example, how can institutions avoid mischaracterizing students with labels such as “at-risk”, and instead situate and serve them…