Spatial skills and mathematics, towards a causal understanding of mechanisms

Difficulties in learning maths usually begin at the start of education and are robust over time.

The number of students not reaching basic levels of maths achievement remains a major societal challenge, at a time when societies are increasingly demanding maths skills due to scientific and industrial progress.

Research examining the key cognitive foundations for successful maths learning has identified spatial skills as a promising potential contributor to maths development. Nevertheless, how and why SPatial skills contribute to mATHS learning remains elusive.

SPATHS will therefore develop a new framework that aim to understand the causal mechanisms via which spatial skills impact maths learning.

This will be done firstly by using graphical causal models, in particular Directed Acyclic Graphs (DAG), to draw causal inferences from observational data.

This technique, originally developed for computer science and novel to cognitive and developmental psychology, is a way of visually representing and testing a priori causal assumptions.

Various spatial skills and maths abilities will be tested and causal pathways between the different abilities will be estimated in a cross-cultural context, which varies in the extent to which children start formal school (start at 5 vs. start at 6-years) and allows us to generalise findings across different educational contexts.

Second, to empirically validate the findings from the causal models, a training experiment will manipulate the learning of spatial skills in 5-and 6-year-olds. SPATHS will unravel long-awaited knowledge about the mechanism underlying the spatial-maths association.

This is essential for future research into efficient interventions to improve maths and to make spatial training programs more effective.

Moreover, by introducing DAGs to cognitive and developmental psychology, SPATHS will provide the possibility to test causality in natural environments (e.g., schools) in which experimental data are difficult to obtain.