What are the barriers that keep students from finishing an application or from completing their degree? Emily Coleman joins me today from HAI Analytics to discuss her company’s work in building predictive models for higher education institutions. These models help predict student behavior to optimize critical processes like the allocation of financial aid. Emily explains the importance of retaining data, overcoming data silos, and incorporating both human and artificial intelligence in predictive analytics. She also provides insights into how predictive models can aid enrollment forecasting, retention strategies, and resource allocation for better student support. Additionally, real-world examples of schools implementing these models and the difference they made are shared. Emily highlights the benefits of data-driven decision-making in effectively managing enrollment and student success.
What you will learn:

• How humans and AI can work together to handle data in the best way.
• Why predictive modeling is a great tool to help meet enrollment, headcount, and revenue goals.
• Examples of predictions that data models from HAI Analytics can make.
• The impact that comes when a college can understand the problems a student faces before they stop attending.

Thanks for listening!
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