Literature DB >> 29691952

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

Iro Xenidou-Dervou1,2, Johannes E H Van Luit3, Evelyn H Kroesbergen3,4, Ilona Friso-van den Bos3, Lisa M Jonkman5, Menno van der Schoot2, Ernest C D M van Lieshout2.   

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.
© 2018 John Wiley & Sons Ltd.

Entities:  

Mesh:

Year:  2018        PMID: 29691952     DOI: 10.1111/desc.12671

Source DB:  PubMed          Journal:  Dev Sci        ISSN: 1363-755X


  5 in total

1.  How language skills and working memory capacities explain mathematical learning from preschool to primary school age: Insights from a longitudinal study.

Authors:  Nurit Viesel-Nordmeyer; Alexander Röhm; Anja Starke; Ute Ritterfeld
Journal:  PLoS One       Date:  2022-06-24       Impact factor: 3.752

2.  What Ability Can Predict Mathematics Performance in Typically Developing Preschoolers and Those with Autism Spectrum Disorder?

Authors:  Lijuan Wang; Xiao Liang; Bo Jiang; Qiutong Wu; Luyao Jiang
Journal:  J Autism Dev Disord       Date:  2022-02-03

3.  When one size does not fit all: A latent profile analysis of low-income preschoolers' math skills.

Authors:  Nicole R Scalise; Emily N Daubert; Geetha B Ramani
Journal:  J Exp Child Psychol       Date:  2021-06-02

4.  How to capture developmental brain dynamics: gaps and solutions.

Authors:  Nienke van Atteveldt; Maaike Vandermosten; Wouter Weeda; Milene Bonte
Journal:  NPJ Sci Learn       Date:  2021-05-03

5.  Effect of Obesity on Arithmetic Processing in Preteens With High and Low Math Skills: An Event-Related Potentials Study.

Authors:  Graciela C Alatorre-Cruz; Heather Downs; Darcy Hagood; Seth T Sorensen; D Keith Williams; Linda J Larson-Prior
Journal:  Front Hum Neurosci       Date:  2022-03-10       Impact factor: 3.169

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.