Pathways to math difficulties -A longitudinal study from birth to school-age

Informacje na temat projektu

EARLYMATH

Identyfikator umowy o grant: 101002966

DOI

10.3030/101002966

Data podpisania przez KE 13 Kwietnia 2021

Data rozpoczęcia 1 Września 2021

Data zakończenia 31 Sierpnia 2026

Finansowanie w ramach

• EXCELLENT SCIENCE - European Research Council (ERC)

Koszt całkowity

€ 1 999 608,00

Wkład UE

€ 1 999 608,00

Koordynowany przez

JYVASKYLAN YLIOPISTO
Finland

Periodic Reporting for period 2 - EARLYMATH (Pathways to math difficulties -A longitudinal study from birth to school-age)

Okres sprawozdawczy: 2023-03-01 do 2024-08-31

Podsumowanie kontekstu i ogólnych celów projektu

The EarlyMath project (2021-2026) studies children’s early development of mathematical skills, motivation and emotions in interaction with learning environments. We want to understand better how mathematical skills develop and why the learning of basic mathematical skills is challenging for ten to twenty percent of children. This knowledge is needed for developing the means of early identification of children who need more support to learn the essential mathematical skills today, as well as for developing more efficient forms of support for learning in daycare settings, schools, and homes.

EarlyMath seeks answers to the following questions:
-What mathematical capacities and skills do small children have?
-What kind of developmental paths can be identified in children’s mathematical skills as well as in the related motivation and emotions?
-Why is learning mathematical skills more difficult for some than it is for others?
-How is mathematics present at home, in early childhood education and care, and at school?
-To what degree do parents’ skills predict their children’s skills?

Prace wykonane od początku projektu do końca okresu sprawozdawczego oraz najważniejsze dotychczasowe rezultaty

EARLYMATH collects three longitudinal datasets; the baby sample, the toddler sample, and the school sample. The school sample is a continuation of our previous longitudinal project and follows children's development from age 2 to age 10. Toddles sample follow children's development from age 2 to 5 and baby sample fro 2 weeks to 3 years. Currently the data collection for the school sample is completed (N=700) and for the toddler sample the age 3.5 years assessments are ongoing (N=450). In the baby sample we have recruited > 80 families and recruitment is ongoing. From the beginning of the project we have started a careful metadata documentation; the metadata for the school sample is ready and can be found in: https://jyx.jyu.fi/handle/123456789/94966.

In addition to the data collection, some first results are published from the school sample. We have identified the profiles of mathematical skills among pre-school children and their predictive associations with school-age arithmetic skills. We have found significant intergenerational transmission in mathematical skills and also established valid ways to identify familial risk factors for children's early mathematical development. We have also established measures for identification of home numeracy environment and their reciprocal associations with young children's developing skills. Based on the results in the school sample we are in the process of further improving measurement for the toddler and baby samples.

Overall, the results are clear on that the process for mathematical skills start early and already at age 2.5 years, children have skills that predict their mathematical development in the coming years. The basic assumptions of the project, that parental mathematical skills predict children's skills and that home environment has reciprocal effects with children's developing skills have been supported. However, the results need to be replicated in other samples.

Innowacyjność oraz oczekiwany potencjalny wpływ (w tym dotychczasowe znaczenie społeczno-gospodarcze i szersze implikacje społeczne projektu)

The project aims to gather three unique datasets regarding the early years of child development in numerical cognition. The datasets will allow us to provide novel information on the research questions. With these information we aim to develop new methods for early identification and support for those in need and in this way improve mathematical skills and motivation development from early years onward.

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