At-Risk Students Intervention

How Data Science Can Help Boost College Graduation Rates

Graduates throwing graduation caps in air.

A data philanthropy project is helping one university identify students at risk of dropping out – and intervene before it’s too late.

With the support of the Mastercard Center for Inclusive Growth and the Robin Hood Foundation, the college commissioned the nonprofit DataKind to develop more than 20 machine-learning models using the John Jay College of Criminal Justice at the City University of New York (CUNY)’s existing data for students with 90-plus credits, to predict the risk of dropout or delayed graduation. This will enable the college to identify students at risk and intervene with advisory services in an effort to increase their chances of graduating. John Jay advisors use the tool as an aide to prioritize interventions, rather than use it to dictate mandatory interventions; this human oversight is critical to ensure ethical outputs from the algorithm.