Key Takeaway: By analyzing student-level data related to early warning indicators, districts can better identify students who are at-risk of dropout.
Attendance, behavior, and coursework are the three strongest indicators of a student’s engagement with school and have therefore been the most used predictors of educational attainment (Allensworth, 2013; Mac Iver & Messel, 2013). An engaged student attends school, exhibits positive behaviors, and earns passing grades. Districts have the power to identify students who are disengaged and intervene quickly by using the data they already collect and store in established systems. Data analysis software that can aggregate and display student-level data can accelerate the identification process allowing districts to locate students who are most at-risk and get them the help they need to get back on track.
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