Developmental pathways of early numerical skills during the preschool to school transition |
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| Institution: | 1. School of Psychology, Ulster University, Coleraine, Londonderry BT52 1SA, UK;2. Department of Health Sciences, University of Leicester, Leicester LE1 6TP, UK;3. School of Psychology, University of Nottingham, University Park, Nottingham NG7 2RD, UK;4. Mathematics Education Centre, Loughborough University, Leicestershire LE11 3TU, UK;1. University of Grenoble Alpes, CNRS, LPNC, 38000 Grenoble, France;2. Department of Psychology, Fribourg Center for Cognition, University of Fribourg, 1700 Fribourg, Switzerland;3. Sensorimotor, Affective and Social Development Unit, University of Geneva, 1211 Geneva, Switzerland |
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| Abstract: | Most longitudinal evidence explores the average level of development, suggesting that the relationships between a limited number of variables applies to all learners in the same way. This is the first longitudinal study that investigates multiple component numeric skills within a preschool population using a person-centered approach (i.e., a latent transition analysis), thus allowing for an investigation of different subgroup learning pathways of mathematical skills over time. 128 children aged 43–54 months (at Time 1) were tracked at three time points over 8 months encompassing the transition from preschool through to their first year of primary education. Findings suggest that there are five developmental pathways of mathematical learning with some groups of children making more rapid progress on entry to school than other groups. Those children in the low number skill pathway have a lower rate of growth than more advanced pathways, possibly due to a lack of understanding in cardinality. Findings highlighted the potential importance of language and working memory abilities on mathematical skills development over time. |
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| Keywords: | Latent transition analysis Developmental pathways of early numerical skills Longitudinal Preschool to school transition Multiple component numeric skills Domain-general and non-cognitive predictors |
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