Early prediction of math difficulties with the use of a neural networks model
Psyridou, M., Koponen, T., Tolvanen, A., Aunola, K., Lerkkanen, M.-K., Poikkeus, A.-M., & Torppa, M. (2024). Early prediction of math difficulties with the use of a neural networks model. Journal of Educational Psychology, 116(2), 212-232. https://doi.org/10.1037/edu0000835
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Journal of Educational PsychologyAuthors
Date
2024Discipline
PsykologiaKasvatuspsykologiaHyvinvoinnin tutkimuksen yhteisöPsychologyKasvatuspsykologiaSchool of WellbeingCopyright
© American Psychological Association 2023
The early prediction of math difficulties (MD) is important as it facilitates timely support. MD are multifaceted, and several factors are involved in their manifestation. This makes the accurate early prediction of MD particularly challenging. In the present study, we aim to predict MD in Grade 6 with kindergarten-age (age 6) measures by applying a neural networks model. We use a set of 49 variables assessed during kindergarten from the domains of early arithmetic skills, cognitive skills, the home learning environment, parental measures, motivation, behavioral problems, and gender, which have been shown to have associations with mathematical development and/or MD. A two-step approach was used: First, we examined whether the neural networks approach can provide a solution for the effective early identification of MD based on all 49 variables and, then, by using the most important predictors as identified by the initial model. The initial model achieved an area under the curve (AUC) of .818, demonstrating excellent performance. The most important predictors of Grade 6 MD came from the domains of arithmetic and cognitive skills (arithmetic skills, rapid automatized naming, number concepts, spatial skills, counting) and behavioral problems (attention-orientation). The model with only the most important predictors achieved an AUC of .776, indicating good performance. Our results provided proof of concept for using neural networks in MD prediction in Grade 6 using information already available in kindergarten. In schools, these results could be used to identify children at potential risk of developing MD and to provide access to early support.
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American Psychological AssociationISSN Search the Publication Forum
0022-0663Keywords
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https://converis.jyu.fi/converis/portal/detail/Publication/194369443
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Related funder(s)
Research Council of Finland; European CommissionFunding program(s)
Postdoctoral Researcher, AoF; Academy Project, AoF; Research costs of Academy Research Fellow, AoF; Research profiles, AoF; Academy Research Fellow, AoF; ERC Consolidator Grant
The content of the publication reflects only the author’s view. The funder is not responsible for any use that may be made of the information it contains.
Additional information about funding
The First Steps study was supported by the Academy of Finland (Grants 213486, 263891, 268586, and 292466). In addition, Maria Psyridou was supported by the Academy of Finland (Grant 339418). Minna Torppa was supported by the Academy of Finland (Grants 276239, 284439, and 313768) and by the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (Grant Agreement 101002966). ...License
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