Early Warning for Whom? Effect of Early Warning System on Student Absence

Early Warning for Whom? Effect of Early Warning System on Student Absence

Policy Brief #24-4April 2024


A recent study finds that use of the Early Warning System (EWS), an intervention designed to address the increase of chronically absent students, may be an insufficient approach for reaching socioeconomically disadvantaged students and emphasizes the importance of efforts beyond isolated school-based initiatives.

Chronic absenteeism has risen in the US since the pandemic. The percentage of chronically absent students, those who miss 10% or more of the school year –either being excused or unexcused— doubled from about 14% to 28% with a disproportionate increase among low-income students (Chang et al, 2022; Dee, 2024). In response to increased absenteeism, schools have adopted several approaches ranging from attendance awareness campaigns to home visits and expanded healthcare access (Attendance Works, 2024).

One of the common approaches schools have implemented to identify chronically absent students and intervene on time is the Early Warning System (EWS). While its low-cost and potentially useful features have stimulated the spread of EWS across the country, evidence of its effectiveness in improving educational outcomes has been limited. In a recent peer-reviewed study, researcher Yusuf Canbolat examined the effect of EWS and found that students` socioeconomic backgrounds matter.

EWS flags students as on-track, at-risk, and off-track based on risk categories. These EWS indicators are often accessible in a data platform for teachers, school administrators, school counselors, and social workers. Using these indicators, schools tailor interventions based on students’ individual needs and monitor their progress. Typically, EWS is an integral part of the multitiered system of support that outlines interventions across tiers in which the target group ranges from the whole school to individual students (Balfanz & Byrnes, 2019; McMahon & Sembiante, 2020).

The focus of this study was the effect of the EWS on student absence in a large urban Southeast school district that has more than eighty thousand students. Exploiting a regression discontinuity design that enabled causal estimation of the EWS effect, the study used data from the school years 2020–2021 and 2021–2022.

Findings indicate that EWS reduces chronic absence among socioeconomically advantaged students by 1.3 percentage points or 22%. However, it has no significant effect on chronic absence among socioeconomically disadvantaged students. Furthermore, EWS has no significant effect on moderate absence since a substantial number of students fall into the moderate absence category posing challenges for schools.

These results suggest that the socioeconomically disadvantaged may face more structural barriers to attending school ranging from lower academic achievement to poorer health conditions or fewer family resources (Sosu et al., 2021). They are also

less likely to have sufficient community support and role models leading to negative attitudes toward school, and poor transportation opportunities (Gottfried, 2014). Other intertwined issues such as high residential mobility, and concentration of economically disadvantaged students in one’s school are associated with higher levels of chronic absence (Singer et al., 2021).

Although the findings are from a single school district and potentially have limited generalizability, an obvious policy implication from this study is that school-based efforts are necessary but may not be sufficient to reduce chronic absence. Shaped by the school-, student-, family-, and community-level factors, the multilayered nature of chronic absence requires extensive efforts to include all levels of the school system and other social sectors within the public system.

This brief is based on a manuscript published as:

Canbolat, Y. (2024). Early Warning for Whom? Regression Discontinuity Evidence From the Effect of Early Warning System on Student Absence. Educational Evaluation and Policy Analysis, 0(0). https://doi.org/10.3102/01623737231221503


Yusuf Canbolat, PhD, is a strategic data fellow at the Center for Education Policy Research at Harvard University.