How Stable Is Early Academic Performance? Using Cluster Analysis to Classify Low Achievement and EF

Abstract

We explored whether and how cognitive measures of executive function (EF) can be used to help classify academic performance in Kindergarten and first grade using nonparametric cluster analysis. We found that EF measures were useful in classifying low-reading performance in both grades, but mathematics performance could be grouped into low, average, and high groups without the use of EF tasks. Membership in the high-performing groups was more stable through first grade than membership in the low or average groups, and certain Kindergarten EF tasks differentially predicted first-grade reading and mathematics cluster membership. Our results suggest a stronger link between EF deficits and low performance than between EF strengths and high performance. We highlight the importance of simultaneously using academic and cognitive skills to classify achievement, particularly since existing classification schemes have been largely based on arbitrary cutoffs using limited academic measures.