An early warning system uses student data and predictive analytics to identify students who may go on to drop out or not graduate on time. Administrators employ early warning systems (EWSs) to help interventionists (e.g., social workers, counselors, specialists) proactively support students to an on-time graduation. While EWSs differ significantly from institution to institution, EWS models include the following core components:
EWSs predict either dropout or on-time graduation.
An EWS flags a student as at-risk when their performance in a variable (e.g., behavior) reaches a certain threshold. For example, an EWS may flag a student as at risk after three discipline referrals.
Some EWSs do not use uniform thresholds. For example, the EWS at Department of Education A uses multivariable regression.
Administrators often use attendance, behavior, and course grades—known as the ABCs—to predict outcomes.
Administrators use student data from past years at their district to determine which variables best predict dropout or delayed graduation.
EWSs assign each student a risk status based on the system’s variables and thresholds/analytics.
EWSs usually use three risk levels: high risk, medium risk, or low risk—often coded as red, yellow, and green.
EAB interviewed contacts at four school districts, two state departments of education, and a non-profit to understand how institutions in various administrative positions operate EWSs. The profiled nonprofit, Nonprofit A, operates an EWS in 19 schools.
Predictive mechanisms identify at-risk studentsMost districts build early warning systems that use attendance, behavior (as measured by discipline incidents), and course grades to estimate the likelihood that a student will go on…