This study examines whether the classification and regression tree (CART) model improves the early identification of students at risk for reading comprehension difficulties compared with the more difficult to interpret logistic regression model. CART is a type of predictive modeling that relies on nonparametric techniques. It presents results in an easy-to-interpret "tree" format, enabling parents, teachers, principals, and school district leaders to better understand how a student is predicted to be at risk. Using data from a sample of Florida public school students in grades 1 and 2 in 2012/13, the study found that the CART model predicted poor performance on the reading comprehension subtest of the Stanford Achievement Test as accurately as logistic regression while using fewer or the same number of variables. This research is motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules used to identify students as at-risk or not at-risk readers. An appendix provides detailed information on the study's data sources and methodology.
ERIC DescriptorsAccuracy, At Risk Students, Classification, Comparative Analysis, Data Analysis, Data Use, Elementary School Students, Grade 1, Grade 2, Identification, Literacy, Models, Nonparametric Statistics, Prediction, Public Schools, Reading Comprehension, Reading Difficulties, Reading Skills, Regression (Statistics)
Southeast | Publication Type: Descriptive Study | Publication
Date: January 2014