A universal screening tool for dyslexia by a web-game and machine learning

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• dc.contributor.author Rauschenberger, Maria
• dc.contributor.author Baeza Yates, Ricardo
• dc.contributor.author Rello, Luz, 1984-
• dc.date.accessioned 2023-03-01T07:23:37Z
• dc.date.available 2023-03-01T07:23:37Z
• dc.date.issued 2022
• dc.description.abstract Children with dyslexia have difficulties learning how to read and write. They are often diagnosed after they fail school even if dyslexia is not related to general intelligence. Early screening of dyslexia can prevent the negative side effects of late detection and enables early intervention. In this context, we present an approach for universal screening of dyslexia using machine learning models with data gathered from a web-based language-independent game. We designed the game content taking into consideration the analysis of mistakes of people with dyslexia in different languages and other parameters related to dyslexia like auditory perception as well as visual perception. We did a user study with 313 children (116 with dyslexia) and train predictive machine learning models with the collected data. Our method yields an accuracy of 0.74 for German and 0.69 for Spanish as well as a F1-score of 0.75 for German and 0.75 for Spanish, using Random Forests and Extra Trees, respectively. We also present the game content design, potential new auditory input, and knowledge about the design approach for future research to explore Universal screening of dyslexia. universal screening with language-independent content can be used for the screening of pre-readers who do not have any language skills, facilitating a potential early intervention.
• dc.format.mimetype application/pdf
• dc.identifier.citation Rauschenberger M, Baeza-Yates R, Rello L. A universal screening tool for dyslexia by a web-game and machine learning. Front Comput Sci. 2022;3:628634. DOI: 10.3389/fcomp.2021.628634
• dc.identifier.doi http://dx.doi.org/10.3389/fcomp.2021.628634
• dc.identifier.issn 2624-9898
• dc.identifier.uri http://hdl.handle.net/10230/55974
• dc.language.iso eng
• dc.publisher Frontiers
• dc.relation.ispartof Frontiers in Computer Science. 2022;3:628634.
• dc.relation.isreferencedby https://www.frontiersin.org/articles/10.3389/fcomp.2021.628634/full#supplementary-material
• dc.rights © 2022 Rauschenberger, Baeza-Yates and Rello. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword dyslexia
• dc.subject.keyword screening tool
• dc.subject.keyword game
• dc.subject.keyword machine learning
• dc.subject.keyword German
• dc.subject.keyword Spanish
• dc.subject.keyword study setup
• dc.subject.keyword online experiment
• dc.title A universal screening tool for dyslexia by a web-game and machine learning
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion