Profiles of Mathematics Learners: Domain-Specific and Domain-General Classifiers

Previous wok has established that multiple academic and cognitive skills can contribute to difficulty with mathematics (Bull & Lee, 2014; Morgan et al., 2019; Purpura Day et al., 2017; Schmitt et al., 2017). However, little is known about how using both domain-specific (i.e., numeracy) and domain-general skills (i.e., executive function (EF), language, vocabulary) can help to identify children at risk for mathematics difficulties. Given that an estimated 3% to 8% of children are diagnosed with a math related disability (Desoete et al., 2004) and even more children experience difficulties with mathematics, and that mathematics is an important predictor of academic and career success (Duncan et al., 2007; Watts, 2020) it is imperative to understand how both domain-specific and domain-general skills, along with key demographic factors, can be used to identify children at risk for future mathematics difficulties. The current study utilized data from a longitudinal project that assessed children’s academic and cognitive skills over four time points: the fall and spring of preschool and kindergarten in a state in the Midwest of the United States.The analytic sample for this study consisted of 674 children from three cohorts. A person-centered latent profile analysis was used to generate profiles of early math learners using children’s early numeracy, math language, executive function (EF), literacy, and vocabulary skills. Based on the model fit statistics and interpretability, a six-profile solution emerged from the data. The latent profile approach was compared to a variable-centered regression for identifying risk for mathematics difficulties. Results show that there is variability in performance profiles in a sample at the lower end of mathematics performance. This study suggests that domain-general skills such as EF, literacy, and mathematical language skills can help to distinguish between different performance profiles of mathematics learners. Results also demonstrate that person-centered and variable-centered approaches should be used in conjunction with one-another to best identify children at risk for mathematics difficulties.