Identifying children with persistent low math achievement: The role of number-magnitude mapping and symbolic numerical processing. (April 2019)

• Record Type: Journal Article
• Authors: Wong, Terry Tin-Yau; Chan, Winnie Wai Lan
• Is Part Of: Learning and instruction. Volume 60(2019)
• Page Start: 29
• Page End: 40
• Publication Date: 2019-04
• Subjects: Dyscalculia -- Mathematics learning disabilities -- Numerical magnitude -- Longitudinal predictors; Learning -- Periodicals; Teaching -- Periodicals; Apprentissage -- Périodiques; Enseignement -- Périodiques; Learning; Teaching; Periodicals; Electronic journals; 370.1
• DOI: 10.1016/j.learninstruc.2018.11.006
• Languages: English
• ISSNs: 0959-4752

Abstract

Abstract: Although an increasing number of research studies have investigated the cognitive deficits related to difficulties in learning mathematics, little is known about whether these cognitive deficits longitudinally predict low mathematics achievement over time. The current 6-year longitudinal study was conducted to address this issue. A sample of 101 students was tested on various numerical and cognitive competencies when they were in kindergarten and in Grade 1. They were then followed until they were in Grade 6, and their mathematics achievement was assessed bi-annually. A group of persistent low mathematics achievers (PLA) who scored consistently below the 25th percentile was identified. This group of PLA showed difficulties in most of the numerical tasks as early as kindergarten. More importantly, three of the early predictors correctly identified 79% of the PLAs. The current findings provide valuable information concerning the core cognitive deficits underlying difficulties in learning mathematics as well as an important tool for educators for identifying children who are at risk of persistent math learning difficulties in the elementary school years. Highlights: A sample of persistent low achiever (PLA) in mathematics was identified. They demonstrated early cognitive deficits at or before Grade 1. They performed worse in number comparison, number line and place value. These measures predicted PLA membership with 79% accuracy. The findings make early identification of PLA possible.