Identify At-Risk Students as Early as First Grade with Over 90% Accuracy

The Brightbytes Early Warning module uses predictive analytics to identify at-risk students on a continuum of risk based on each organization's actual data.


Timely, Individualized Risk Prediction and Prevention

Decrease risk factors and increase student outcomes with next generation predictive analytics. Change the trajectory of students' lives by identifying students at risk of dropping out based on a dynamic combination of each district's historical data and data from the SIS. 


Discover the Benefits of Early Warning


A Comprehensive Guide to Predictive Analytics



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The Early Warning Framework

The Early Warning framework consists of four domains supported by 24 Success Indicators that are associated with the risk factors within each organization’s model.

Monitor major and minor behavioral incidents, including referrals and expulsions

Review student profiles of age, 504 status, mobility, gender, and more

Analyze GPA, assessment scores, pass rate, and course requirements

Access tardies, attendance in the first 30 days, and attendance overall


What Our Customers Say

BrightBytes’ research-based predictive algorithm gives us the ability to customize the identification of student needs and provide resources that ensure student success. It provides us with a micro and macro view of our district, and allows us to make more data driven decisions and connect research to practice.”
I really feel that BrightBytes is a fantastic tool for targeting at-risk students. This would have been extremely beneficial in catching my granddaughter in elementary school and applying interventions earlier.”