Giving Back to Them on Their Terms: Predictive Personalization

Nearly 2 million students who begin college each year will drop out before earning a diploma from their initial institution. The average institution loses $9,910,811 in revenue over the expected lifetime student value due to student attrition. Today’s students not only expect a lot of traditional support services from institutions, they also capitalize on sophisticated offerings of AI, mixed reality, and other immersive and emerging technologies. At Kaplan, we are using machine-learning models to identify at-risk students and develop personalized intervention strategies to help them persist.

Demo the model to show how the prescriptive tool helps monitor the reserve students’ pipeline *Identify students who have a high probability of dropping out *Inform what is driving the changes in drops *Understand characteristics today’s student possess and their unique needs *Prescribe an effective outreach strategy