Can analytics help decrease attrition in higher education?
Student attrition is a serious concern for colleges and universities around the world. Although the numbers vary slightly, the common estimate worldwide is that one in five (20 percent) of first year students drop out or leave the institution they first enrolled in, having an impact on the institution’s reputation, and its bottom line. According to a survey conducted by the Education Policy Institute in 2012 (surveying 1,669 US colleges and universities on the cost of attrition), the cost of attrition at these institutions was $16.5 billion total. Yes, billion. That averages out to a cost of $9.9 million in lost revenue among these institutions. And the US is not alone—similar numbers and are occurring around the world.
The simple answer is yes, but first it is important to understand the factors that lead a student to leave. Certainly academic factors come into play. It is generally acknowledged that a student’s inability to perform academically is a chief cause of attrition. But it’s often not just academic factors that impact a student. The cost of attending college and university is skyrocketing, putting a huge financial burden on students. Health and mental issues also may drive a student’s decision not to return. How many of us were away from home for the first time when we left for college? This is a very challenging experience to couple with the demands of higher education.
Analytics and data can help higher education institutions manage attrition by helping to align all data to get a comprehensive view of each student, and then using analytics to determine who may be at risk. Once identified, those students’ issues that could lead to attrition can be addressed, either through additional academic counseling, financial aid support or health and mental health services.
This process isn't easy. It requires aligning data from multiple, often disparate sources for that comprehensive view of the student. One must then analyze and consume that data through the application of analytics, such as dashboards, reports and predictive models to find patterns that identify those students at risk of attrition. After this analysis comes the intervention and follow-up of tracking student progress.
Is this a vision of the future of education?
Today we see many institutions addressing their attrition issues through the use of analytics and data. Marist College is doing it today, achieving a 75 to 85 percent accuracy in predicting students at risk and intervening quickly. A college in the United Kingdom is using data and analytic capabilities to drive a 15 percent reduction in attrition, in a nation that has the lowest student attrition rates.
Today the promise of analytics-driven approaches to managing and lowering student attrition is real and happening every day.