This session explores how attendance data can be used as an early warning indicator to predict academic risk before test scores become available. Using a predictive model created in Python, variables such as attendance rate, days absent, tardies, chronic absenteeism status, and prior assessment scores can help to identify students at risk of not meeting academic benchmarks and therefore requiring academic intervention and support. Participants of this session will learn how predictive modeling can inform data driven decision making.
