Abstract
Math learning difficulties (MD) correspond to math achievement below the 25th percentile and are cognitively heterogeneous. It is not known precisely how cognitive mechanisms underlie distinct subtypes of MD. A bottom-up, cluster-analytic strategy, based on visuoconstructional, visuospatial and phonological working memory, and non-symbolic and symbolic magnitude processing accuracy, was used to form subgroups of children from 3rd to 5th grades according to their math achievement. All children had nonverbal intelligence above the 20th percentile and presented a broad spectrum of variation in math ability. External validity of subgroups was examined considering intelligence and math achievement. Groups did not differ in age. Two groups with a high incidence of MD were associated, respectively, with low visuospatial/visuoconstructional and low magnitude processing accuracy. One group with average cognitive performance also presented above average intelligence and a small incidence of MD. A fourth group with high cognitive performance presented high math performance and high intelligence. Phonological working memory was associated with high but not with low math achievement. MD may be related to complex patterns of associations and dissociations between intelligence and specific cognitive abilities in distinct subgroups. Consistency and stability of these subgroups must be further characterized. However, a bottom-up classification strategy contributes to reducing the cognitive complexity of MD.
Highlights
Math learning difficulties (MD) correspond to math achievement below the 25th percentile and are cognitively heterogeneous
We investigated the frequency of MD in each cluster, and we performed a series of ANCOVAs, with intelligence as a covariate, using the z-scores of the TDE Arithmetic subtest, as well as the single digit operations as dependent variables
To identify possible subgroups of cognitive performance that could be eventually associated with math achievement, we used a bottom-up strategy
Summary
Math learning difficulties (MD) correspond to math achievement below the 25th percentile and are cognitively heterogeneous It is not known precisely how cognitive mechanisms underlie distinct subtypes of MD. A bottom-up, cluster-analytic strategy, based on visuoconstructional, visuospatial and phonological working memory, and non-symbolic and symbolic magnitude processing accuracy, was used to form subgroups of children from 3rd to 5th grades according to their math achievement. Familial, pedagogical and social factors, individual differences among typical and atypical math achievers are influenced by neurocognitive factors These factors include basic number, phonological, visuospatial/visuoconstructional, working memory/executive functions processing (Wilson & Dehaene, 2007), and motivational/emotional self-regulation (Dowker, Sarkar, & Looi, 2016). More liberal criteria, such as 25th percentile, could be associated with a difficult to characterize patterns of cognitive impairment among these children (Mazzocco, 2007)
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