Number-specific and general cognitive markers of preschoolers’ math ability profiles

Gray, Sarah A. O.; Reeve, Robert A.

doi:10.1016/j.jecp.2016.02.004

Cited by 56 publications

(72 citation statements)

References 91 publications

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“…The existence of SFON and its association with mathematical ability has been replicated in many studies (see, Rathé, Torbeyns, Hannula‐Sormunen, De Smedt, & Verschaffel, ; for a review). Studies have also tested psychometric properties of SFON tasks (e.g., Batchelor, ), explored the association between SFON and mathematical difficulties (e.g., Gray & Reeve, ; Kucian et al ., ), and investigated SFON in a diversity of cultural settings (Batchelor, Inglis, & Gilmore, ; Bojorque, Torbeyns, Hannula‐Sormunen, Van Nijlen, & Verschaffel, ; Edens & Potter, ; Rathé, Torbeyns, Hannula‐Sormunen, & Verschaffel, ; Sella, Berteletti, Lucangeli, & Zorzi, ). Although SFON has recently been identified as an important factor for explaining individual differences in early mathematical development, little research has been done on the potential mechanism by which this SFON tendency contributes to mathematics achievement.…”

Section: Introductionmentioning

confidence: 99%

Verbal and action‐based measures of kindergartners' SFON and their associations with number‐related utterances during picture book reading

Rathé

Torbeyns

Smedt

et al. 2017

Brit J of Edu Psychol

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Our findings indicate that children with higher SFON as measured by a verbal experimental task also tend to focus more frequently on number during verbal everyday activities, such as picture book reading. In view of the divergent associations between our SFON measures under study with everyday number activities, the current data suggest that SFON may not be a unitary construct and/or might be task-dependent.

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Section: Introductionmentioning

confidence: 99%

Verbal and action‐based measures of kindergartners' SFON and their associations with number‐related utterances during picture book reading

Rathé

Torbeyns

Smedt

et al. 2017

Brit J of Edu Psychol

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“…As evidenced by different SFON measures, individual differences in self-initiated practice using numerical skills can occur in different activities, such as copying pictures as well as performing different actions like feeding a bird, selecting the right number of socks for different monster toys, or describing pictures (Gray & Reeve, 2016;Batchelor et al, 2015;, thus children do not use and learn mathematical skills only in adult-guided explicitly mathematical tasks. Recent research suggested considerable contextual effects and variability across different kinds of SFON measures (Batchelor et al, 2015;Rathé, Torbeyns, Hannula-Sormunen, & Verschaffel, 2016).…”

Section: Spontaneous Focusing On Numerosity (Sfon)mentioning

confidence: 99%

The effect of first school years on mathematical skill profiles

Nanu

Laakkonen

Hannula-Sormunen

2020

FLR

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This study investigated the effect of children's first formal school years on mathematical skill profiles, measured by a variety of arithmetical skills and Spontaneous Focusing On Numerosity (SFON) tasks. By using person-centered approach the aim was to investigate whether the amount of formal schooling is associated with mathematical skills in the same way for all children, or, whether the associations differ according to the children's mathematical skill profiles. Data was analyzed from 652 4-7-year-old children from four European countries with different school entrance ages. A personcentered approach with latent profile regression analyses on four-factor score variables identified six mathematical skill profiles with both qualitative and quantitative differences. The results revealed significant, but small effects of the amount of schooling on mathematical profiles when chronological age and country-specific school entrance age were controlled for. Educational implications of the findings emphasize regarding the heterogeneity in children's mathematical skill profiles and the potentially different effects of starting formal schooling across different profiles.

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“…The limitations of the top-down approach have led some researchers to pursue the data-driven or bottom-up approach, which consists of letting the groups emerge from multivariate techniques of classification, such as cluster analysis (Archibald, Cardy, Joanisse, & Ansari, 2013;Bartelet, Ansari, Vaessen, & Blomert, 2014;Gray & Reeve, 2016;Osmon, Smerz, Braun, & Plambeck, 2006;Peake, Jiménez, & Rodríguez, 2017;Pieters, Roeyers, Rosseel, Van Waelvelde, & Desoete, 2015;Reeve, Reynolds, Humberstone, & Butterworth, 2012;Vanbinst et al, 2015;von Aster, 2000). Ideally, subgroups of individuals with intragroup similarities and between-group differences on some criterion variables should emerge.…”

Section: The Bottom-up Approachmentioning

confidence: 99%

“…Moreover, a few studies have employed a cross-sectional, bottom-up approach focusing on individuals with low math achievement (Bartelet et al, 2014;Gray & Reeve, 2016;Newton & Penner-Wilger, 2015;Osmon et al, 2006;Peake, Jiménez, & Rodríguez, 2017;Pieters et al, 2015;von Aster, 2000). In a study conducted by Peake et al (2017), subtypes of MD were investigated and interpreted considering the Triple Code Model (Dehaene & Cohen, 1995).…”

Section: The Bottom-up Approachmentioning

confidence: 99%

Cognitive heterogeneity of math difficulties: A bottom-up classification approach

et al. 2019

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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.

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Number-specific and general cognitive markers of preschoolers’ math ability profiles

Cited by 56 publications

References 91 publications

Verbal and action‐based measures of kindergartners' SFON and their associations with number‐related utterances during picture book reading

Verbal and action‐based measures of kindergartners' SFON and their associations with number‐related utterances during picture book reading

The effect of first school years on mathematical skill profiles

Cognitive heterogeneity of math difficulties: A bottom-up classification approach

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