Using Early Warning Systems to Flag and Prevent School Failure: A Practical Guide 

Early Warning Systems

Despite growing investments in education, student dropout rates continue to be a global issue. In the United States, 1 in 5 students do not complete high school. The risk of not graduating is even higher for students from marginalized demographic groups, where high 9th-grade retention rates increase the chance of dropping out before graduation. In some schools, labeled “dropout factories,” the student population decreases more than 40% from 9th to 12th grade.

Early warning systems (EWSs) aim to identify students at risk of not graduating on time, enabling schools to implement targeted interventions that reduce dropout rates and support positive student achievement. A well-implemented EWS uses a systems-level approach to integrate key data points to understand areas where support and improvements are needed.

There is no one-size-fits-all approach to developing an effective EWS; districts may use different indicators to measure, assess, and understand the prevalence of risk factors for not graduating among their population of students. Nevertheless, some key recommendations and guidelines can be used in developing an EWS to ensure the adequate allocation and implementation of resources to prevent student dropout.

Research on EWS

Research on EWSs in schools demonstrates their effectiveness in identifying students at risk of academic failure or dropping out. These systems have shown promise across several studies:  

• One study found that middle and high schools implementing EWSs had significantly more students without early warning indicators at the end of sixth and ninth grade; the most substantial impacts were observed in sixth grade, where chronic absenteeism was significantly reduced⁵. 

• Faria and colleagues provided further evidence in a study involving 73 high schools, revealing that EWSs had a significant positive effect on reducing chronic absenteeism and course failures. 

• Similarly, Davis and colleagues found that high schools with strong implementation of the core EWS components in ninth grade experienced greater improvements in student attendance and course performance. 

These studies underscore the promise of EWS in helping schools identify and support at-risk students early, leading to better academic and behavioral outcomes. Despite positive results, challenges remain in fully integrating EWS into school systems, including ensuring equity in support and addressing resource constraints for effective intervention implementation.

Early Warning Indicators (EWIs)

In a Multi-Tiered System of Supports (MTSS), academic and behavioral data are leveraged to guide decision-making and help educators meet the needs of all students. Thus, EWSs can be used within an MTSS to provide educators with the information they need to identify students needing additional support or intervention. Nevertheless, many educators struggle to integrate their EWS within their MTSS practices, and as a result, these systems remain separate. 

EWS and Branching Minds

Branching Minds’ MTSS Solution helps educators proactively identify and support at-risk students by pulling together crucial data points for student success and making them visible across a student’s support network. Our Early Risk Report functionality gives teachers insights into student risk levels across attendance, behavior, and academic performance—data points that research consistently shows to be predictive indicators of student dropout. This approach supports the success of the whole student, allowing individual teachers and school systems to see where their student body is at risk, understand the barriers, and proactively deploy resources and interventions for the students in greatest need of support. 

Essie Sutton, PhD

Dr. Sutton is an Applied Developmental Psychologist and the Director of Learning Science at Branching Minds. Her work brings together the fields of child development and education psychology to improve learning and development for all students. Dr. Sutton is responsible for studying the impacts of the Branching Minds platform on students’ academic, behavioral, and social-emotional outcomes. She also manages and curates the platform’s library of evidence-based supports and studies how different strategies, programs, and tools impact students’ educational outcomes.

Dr. Sutton received her Ph.D. in Applied Developmental Psychology from Fordham University, where her research focused on evaluating academic and social-emotional learning programs in elementary classrooms. Her work has also examined how classroom contexts and the quality of teacher-student interactions influence students’ learning and social development. She has also developed and studied assessments used to measure indicators of children’s well-being and teachers’ classroom practices. Dr. Sutton’s research has been published in Child Indicators Research and the Journal of Applied Developmental Science. She has also presented her work at national conferences, including the American Education Research Association and the Society for Research on Child Development. Dr. Sutton also has an M.A. in Human Development, Learning, and Culture from the University of British Columbia and a B.A. from McGill University.