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RIDE Develops Early Warning System That Allows for Early Intervention

PROVIDENCE, R.I. - The Rhode Island Department of Education (RIDE) today released an updated Early Warning System (EWS), a tool built with the help of The Policy Lab at Brown University that will identify students at risk of dropping out. The EWS tool will be accessible to Rhode Island educators with access to RIDEmap – an internal data portal available to superintendents, principals, and most teachers – allowing them to take action earlier and provide additional supports to our most vulnerable students.  


“The Early Warning System is a fantastic example of how RIDE is shifting from an agency focused on compliance to one focused on support. This tool will allow our partners in the field not only to identify students who are at risk, but also work to ensure that all students are on track to be college and career ready,” said Angélica Infante-Green, Commissioner of Elementary and Secondary Education. “Much of this data is already available, but the tool brings it together into a coherent system that is informative and, most importantly, actionable for educators. We're grateful to The Policy Lab for their partnership on this exciting project, and we look forward to continuing to work with them and with educators statewide to improve the Early Warning System.”


The EWS presents 5 historical data point “indicators” for students in grades 6-12, including school attendance, whether or not they’ve been held back a grade, number of suspensions, and performance on student assessments. Students are assigned a color-coded “risk band” for each of the indicators. DataSpark at the University of Rhode Island supported early research and data analysis for the tool. The Policy Lab then developed a sixth indicator by creating an algorithm to look at even more data, showing the statistical likelihood of drop-out.


“This is exactly the kind of innovative, student-centered work that the state should be doing to support schools and set both teachers and students up for success,” said Barbara S. Cottam, Chair of the Rhode Island Board of Education. “Rather than just looking at and evaluating data once a year, the Early Warning System allows educators to be more proactive in their approach, offering early interventions and supports to get all students on the right track.”


Educators can sort the data and risk bands by indicator and by student sub-group, and can also search by student name. Student-level data, which is available only to the teachers and school leaders serving that individual student, shows not only risk bands for each indicator, but also links to supportive resources and interventions that school teams may consider deploying to reduce risk of drop-out. 


“Data can empower educators, school leaders, and policymakers to improve the lives of Rhode Island residents,” said David Yokum, Director of The Policy Lab. “However, this data is only useful if it’s directly informed by the needs and experiences of these on-the-ground stakeholders. I’m proud of The Policy Lab’s work with RIDE and dozens of teachers and administrators to determine what data, statistical models, and design would best help them help students succeed.”


RIDE partnered with East Providence in January to test with educators and administrators, getting their first-hand feedback on how to improve the experience and effectiveness of the tool. The EWS is now available statewide as a public beta, and will be revised based on additional feedback from the field over the next six months.


“At East Providence High School, we pride ourselves on knowing our students,” said Shani Wallace, Principal of East Providence High School. “The Early Warning System is an essential component in the development of a complete picture of each member of our student body.  It allows us to personalize supports to keep our diverse students in school and on track to graduate.”


The EWS platform incorporates links to resources and possible interventions alongside each data point, giving educators access to immediate, actionable data and next steps.


To learn more about the RIDE Early Warning System and to watch a demonstration video of the tool that uses generic sample data, please visit our website.