Using evidence-based decision trees instead of formulas to identify at-risk readers

Educators need to understand how students are identified as at risk for reading problems. This study found that the classification and regression tree (CART) model—a type of predictive modeling that presents results in an easy-to-interpret “tree” format—predicted poor performance on the reading comprehension subtest of the Stanford Achievement Test as accurately as the logistic regression model, which is more difficult to interpret. The CART model’s ease of communication enables parents, teachers, principals, and school district leaders to better understand how a student is predicted to be at risk.