Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage
Abstract Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children’s lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems,...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2022-12-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-26038-9 |
| _version_ | 1811198379496046592 |
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| author | Linda Greta Dui Eugenio Lomurno Francesca Lunardini Cristiano Termine Alessandro Campi Matteo Matteucci Simona Ferrante |
| author_facet | Linda Greta Dui Eugenio Lomurno Francesca Lunardini Cristiano Termine Alessandro Campi Matteo Matteucci Simona Ferrante |
| author_sort | Linda Greta Dui |
| collection | DOAJ |
| description | Abstract Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children’s lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts’ evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible. |
| first_indexed | 2024-04-12T01:29:58Z |
| format | Article |
| id | doaj.art-ad2acedf482e4764bbe2f1afd935b819 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-12T01:29:58Z |
| publishDate | 2022-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ad2acedf482e4764bbe2f1afd935b8192022-12-22T03:53:31ZengNature PortfolioScientific Reports2045-23222022-12-0112111410.1038/s41598-022-26038-9Identification and characterization of learning weakness from drawing analysis at the pre-literacy stageLinda Greta Dui0Eugenio Lomurno1Francesca Lunardini2Cristiano Termine3Alessandro Campi4Matteo Matteucci5Simona Ferrante6Department of Electronics, Information and Bioengineering, Politecnico di MilanoDepartment of Electronics, Information and Bioengineering, Politecnico di MilanoChild Neuropsychiatry Unit, Fondazione IRCCS Istituto Neurologico Carlo BestaChild Neuropsychiatry Unit, Department of Medicine and Surgery, University of InsubriaDepartment of Electronics, Information and Bioengineering, Politecnico di MilanoDepartment of Electronics, Information and Bioengineering, Politecnico di MilanoDepartment of Electronics, Information and Bioengineering, Politecnico di MilanoAbstract Handwriting learning delays should be addressed early to prevent their exacerbation and long-lasting consequences on whole children’s lives. Ideally, proper training should start even before learning how to write. This work presents a novel method to disclose potential handwriting problems, from a pre-literacy stage, based on drawings instead of words production analysis. Two hundred forty-one kindergartners drew on a tablet, and we computed features known to be distinctive of poor handwriting from symbols drawings. We verified that abnormal features patterns reflected abnormal drawings, and found correspondence in experts’ evaluation of the potential risk of developing a learning delay in the graphical sphere. A machine learning model was able to discriminate with 0.75 sensitivity and 0.76 specificity children at risk. Finally, we explained why children were considered at risk by the algorithms to inform teachers on the specific weaknesses that need training. Thanks to this system, early intervention to train specific learning delays will be finally possible.https://doi.org/10.1038/s41598-022-26038-9 |
| spellingShingle | Linda Greta Dui Eugenio Lomurno Francesca Lunardini Cristiano Termine Alessandro Campi Matteo Matteucci Simona Ferrante Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage Scientific Reports |
| title | Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage |
| title_full | Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage |
| title_fullStr | Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage |
| title_full_unstemmed | Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage |
| title_short | Identification and characterization of learning weakness from drawing analysis at the pre-literacy stage |
| title_sort | identification and characterization of learning weakness from drawing analysis at the pre literacy stage |
| url | https://doi.org/10.1038/s41598-022-26038-9 |
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