Cognitive predictors of children's development in mathematics achievement: A latent growth modeling approach

Abstract

Research has identified various domain-general and domain-specific cognitive abilities as predictors of children's individual differences in mathematics achievement. However, research into the predictors of children's individual growth rates, namely between-person differences in within-person change in mathematics achievement is scarce. We assessed 334 children's domain-general and mathematics-specific early cognitive abilities and their general mathematics achievement longitudinally across four time-points within the first and second grades of primary school. As expected, a constellation of multiple cognitive abilities contributed to the children's starting level of mathematical success. Specifically, latent growth modeling revealed that WM abilities, IQ, counting skills, nonsymbolic and symbolic approximate arithmetic and comparison skills explained individual differences in the children's initial status on a curriculum-based general mathematics achievement test. Surprisingly, however, only one out of all the assessed cognitive abilities was a unique predictor of the children's individual growth rates in mathematics achievement: their performance in the symbolic approximate addition task. In this task, children were asked to estimate the sum of two large numbers and decide if this estimated sum was smaller or larger compared to a third number. Our findings demonstrate the importance of multiple domain-general and mathematics-specific cognitive skills for identifying children at risk of struggling with mathematics and highlight the significance of early approximate arithmetic skills for the development of one's mathematical success. We argue the need for more research focus on explaining children's individual growth rates in mathematics achievement.

Can abacus course eradicate developmental dyscalculia

“…Additionally, our findings shed some light on the policy and practice of education for students in primary school. Arithmetic computation is an important skill throughout primary education and is particularly critical in the early years (Geary, 2011; Xenidou‐Dervou et al, 2018). No students were diagnosed with DD in abacus classes, and the DD rate in the abacus classes was lower than that in the control classes, suggesting that abacus course might be utilized in normal classroom settings to promote arithmetic development in those at high‐risk of DD, as well as those with normal to high mathematics abilities.…”

Exploring mechanisms of rapid automatized naming to arithmetic skills in Chinese primary schoolers

“…This observation may occur because the way RAN is related to children's math performance may reflect influences of biological processes (e.g., cognitive maturation). Indeed, children's math performance is influenced by a mixture of cultural and biological factors (Geary, 1995(Geary, , 2011. In regard to arithmetic fluency, biological factors tend to predict children's accuracy whereas cultural factors tend to predict chosen strategies (Geary, Bow-Thomas, Liu, & Siegler, 1996).…”

Interaction between domain-specific and domain-general abilities in math´s competence

“…According to Carey (2009) and Szkudlarek & Brannon (2017) improving math depends on a multifaceted approach so, ANS acuity is a foundational skill but insufficient. Many studies focused on different domain general abilities as predictors of children individual mathematical achievement: fluid intelligence and working memory (Blankenship et al, 2018;geary, 2011;Xenidou-Dervou et al, 2018), processing speed (Clark et al, 2014;Kuzmina, Tikhomirova, lysenkova & Malykh, 2020), vocabulary and word recall (Purpura & ganley, 2014), inhibitory control (gilmore et al, 2013) or in a combination of three: intelligence, central executive and reading achievement in a seven-year longitudinal study (geary, Nicholas, li & Sun, 2017).…”

“…working Memory (wM) has shown to be a strong longitudinal predictor of various mathematical skills (hornung, Schiltz, Brunner & Martin, 2014;Xenidou-Dervou et al, 2018). According to Baddeley (2012), wM is an attention-driven multicomponent cognitive construct, an active system for temporal storing and processing information in an online manner at service of complex cognitive tasks.…”