Anecdotal evidence and national statistics show that educational outcomes for poor and minority children are generally worse than that of their peers, but they do not explain why. Fortunately, recent research has identified consistent academic factors that more accurately predict whether or not a student is likely to drop out. “Using Early-Warning Data to Improve Graduation Rates: Closing Cracks in the Education System,” a new brief from the Alliance for Excellent Education, explores the predictive power of early-warning data and discusses policies that can support these efforts. It also examines efforts underway in places such as Boston, Chicago, and Louisiana to build early-warning data systems and to apply the information strategically to guide secondary school interventions across the country.
“When a car’s warning light goes on, we immediately seek help,” said Bob Wise, president of the Alliance for Excellent Education and former governor of West Virginia. “Many students heading back to school this month are already showing warning signs that require immediate action. Dropping out is not inevitable. The sooner we can use academic data to identify at-risk students early in their education, and then use the same data to meet their needs, the more likely they are to stay in school and graduate. The good news is that we can; the urgent news is that we must.”
According to the brief, many dropouts share a number of academic characteristics. For example, students who drop out have usually received a failing grade in core courses (especially in math or English), earned a low grade point average (GPA), or scored low on achievement tests. They were often retained a grade because they had not earned enough credits to be promoted and, as a result, were often older than the other students in their class. Furthermore, as demonstrated by low attendance rates and disciplinary problems, these students were frequently not engaged in their education or aware of its importance to future opportunities.
When analyzed in combination, these academic characteristics can provide strong indications of which students are at risk of becoming dropouts. The brief notes several studies that have been able to accurately predict as many as 80 percent of future dropouts by using these indicators.
However, simply identifying at-risk students does nothing to mitigate their risk factors and help them graduate. Instead, as the brief notes, “the power of early-warning indicators lies in the willingness and capacity of school leaders and educators to transform insightful data into strategic decisionmaking that leads to improved student outcomes.” Therefore, while educators cannot change a student’s socioeconomic status, they can certainly work to prevent students from accumulating the academic risk factors of dropping out, and for students who do fall off track, strategically target their unique academic challenges.
The brief also describes how early-warning data can help decisionmakers at the school and district levels. First, early-warning data can be used to better understand the nature of the academic problems in low-performing secondary schools and to unearth systemic weaknesses and enable schools and districts to address them. Second, early-warning data can be used in real-time to assess the effectiveness of strategies in a timely manner. Third, early-warning data provides a way to demonstrate whether an entire school is on track to improving graduation rates. Lastly, easy-to-understand early-warning data can be a powerful tool for garnering support from key stakeholders—including students, parents, and community members—for needed interventions.
In addition to examining how early-warning data can help inform decisions, the brief outlines policies that can support their implementation. It argues that while the real test for using early-warning data exists at the school level, there is much that policymakers and external partners can do to ensure that schools have the time, expertise, and technological tools to analyze and communicate student data, select indicators and triggers, identify at-risk students, and train and support school staff to maximize the power of these systems.
The brief argues that “partners and policymakers at all levels can—and must—play valuable roles in the development and use of early-warning data systems to improve student outcomes.” Some actions it suggests include creating the infrastructure to predict future dropouts, building capacity to implement early-warning and intervention systems, and bringing efforts to scale.
The complete brief is available here