Handwriting difficulties need to be addressed early to avoid several problems to children, both at school and in everyday life, but dysgraphia diagnosis cannot be performed before handwriting maturation. To solve this issue, we hypothesize that the analysis of drawings produced in a preliteracy stage can predict handwriting problems that will occur years later. We designed a three-year longitudinal study from the last year of kindergarten to the end of second grade with two aims: (1) to longitudinally assess the evolution of drawing features, and (2) to understand if the features collected at preliteracy can predict future handwriting problems. Hence, features were tested for statistically significant variation among the five time points available to assess their longitudinal evolution in time. Moreover, we trained machine learning models to select the most important features collected at preliteracy and to assess their predictive capabilities, with dysgraphia risk assessed at the end of second grade. 202 children completed the longitudinal study. We found that 81% of the feature was sensitive to longitudinal maturation and that it is possible to predict the difficulties with a weighted area under the precision-recall curve of 0.72. This is a step forward towards an early intervention for handwriting problems.

Can Free Drawing Anticipate Handwriting Difficulties? A Longitudinal Study

Dui, LG;Toffoli, S;Speziale, C;Matteucci, M;Ferrante, Simona
2022-01-01

Abstract

Handwriting difficulties need to be addressed early to avoid several problems to children, both at school and in everyday life, but dysgraphia diagnosis cannot be performed before handwriting maturation. To solve this issue, we hypothesize that the analysis of drawings produced in a preliteracy stage can predict handwriting problems that will occur years later. We designed a three-year longitudinal study from the last year of kindergarten to the end of second grade with two aims: (1) to longitudinally assess the evolution of drawing features, and (2) to understand if the features collected at preliteracy can predict future handwriting problems. Hence, features were tested for statistically significant variation among the five time points available to assess their longitudinal evolution in time. Moreover, we trained machine learning models to select the most important features collected at preliteracy and to assess their predictive capabilities, with dysgraphia risk assessed at the end of second grade. 202 children completed the longitudinal study. We found that 81% of the feature was sensitive to longitudinal maturation and that it is possible to predict the difficulties with a weighted area under the precision-recall curve of 0.72. This is a step forward towards an early intervention for handwriting problems.
2022
Proceedings of 2022 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)
978-1-6654-8791-7
dysgraphia
free drawing
early intervention
statistics
machine learning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1233170
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