Dropout Prevention: Documenting the Impact of the Early Warning Intervention Monitoring System on Schools and Students
*Names in bold indicate Presenter
To counter the low rates of on-time graduation, educators and researchers have worked to develop dropout prevention approaches that can be implemented inside or outside of school. Some research on these programs suggests that dropout prevention programs can positively impact school persistence and some general strategies are effective for preventing high school dropout, such as strengthening adult and student relationships, embedding academic support for all students into the regular school schedule, and increasing the rigor and pace of coursework. Another promising strategy involves early identification of students who show signs of risk for not graduating on time. States, districts, and schools are developing and using early warning systems for this purpose.
The intent of an early warning system is to systematically use data to identify students who are at risk for not graduating on time and match identified students with interventions to help them get on track. Although schools across the nation are currently using early warning systems to organize their dropout prevention efforts, the effectiveness of this approach on student outcomes has not yet been widely studied.
To fill this gap, the Regional Educational Laboratory (REL) Midwest Dropout Prevention Research Alliance is conducting an efficacy study in high schools across three states to test the impact of the early warning intervention monitoring system (EWIMS) on school and student outcomes. The EWIMS is model was designed to systemically coordinate state, district, and school dropout prevention initiatives. The EWIMS model includes: (1) a validated early warning data tool with multiple reporting features for identifying students at risk, assigning interventions, and monitoring students’ progress; and (2) multifaceted guidance and site-based support to implement a seven-step implementation process that explicitly encourages schools to make data-driven decisions. Participating in the trial are 73 high schools serving over 90,0000 students. Schools were randomly assigned to either start using the EWIMS model in fall 2014 (treatment) or Fall 2015 (control). Although the study is still ongoing, this paper will consider the implications of findings from recent studies of dropout prevention interventions, including those of the other papers in the symposium, for the implementation of the EWIMS model, given that successful implementation relies on schools assigning effective interventions to students who show signs of risk for academic failure. As the field continues to struggle to identify interventions that reliably work for at-risk students, including mentoring and different credit recovery options, there continues to be a lack of clear guidance within early warning system implementation about exactly which interventions to assign to students identified by the system. The authors will engage with the other panel members and audience in a discussion of these challenging and critical issues.