A1 Journal article (refereed)
Early prediction of math difficulties with the use of a neural networks model (2024)

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

JYU authors or editors

Publication details

All authors or editors: Psyridou, Maria; Koponen, Tuire; Tolvanen, Asko; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Poikkeus, Anna-Maija; Torppa, Minna

Journal or series: Journal of Educational Psychology

ISSN: 0022-0663

eISSN: 1939-2176

Publication year: 2024

Volume: 116

Issue number: 2

Pages range: 212-232

Publisher: American Psychological Association

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1037/edu0000835

Publication open access: Not open

Publication channel open access: 

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/92780

Abstract

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.

Keywords: arithmetic; mathematics; mathematical skills; preschoolers (age group); neural networks (information technology); learning; learning difficulties; forecasts

Free keywords: arithmetic; math difficulties; prediction; neural networks model; kindergarten age

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Ministry reporting: Yes

VIRTA submission year: 2023

JUFO rating: 3