Predicting risk of dyslexia with an online gamified test

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• dc.contributor.author Rello, Luz, 1984-
• dc.contributor.author Baeza Yates, Ricardo
• dc.contributor.author Ali, Abdullah
• dc.contributor.author Bigham, Jeffrey P.
• dc.contributor.author Serra Burriel, Miquel
• dc.date.accessioned 2021-03-31T07:20:21Z
• dc.date.available 2021-03-31T07:20:21Z
• dc.date.issued 2020
• dc.description.abstract Dyslexia is a specific learning disorder related to school failure. Detection is both crucial and challenging, especially in languages with transparent orthographies, such as Spanish. To make detecting dyslexia easier, we designed an online gamified test and a predictive machine learning model. In a study with more than 3,600 participants, our model correctly detected over 80% of the participants with dyslexia. To check the robustness of the method we tested our method using a new data set with over 1,300 participants with age customized tests in a different environment -a tablet instead of a desktop computer- reaching a recall of over 78% for the class with dyslexia for children 12 years old or older. Our work shows that dyslexia can be screened using a machine learning approach. An online screening tool in Spanish based on our methods has already been used by more than 200,000 people.
• dc.description.sponsorship Financial support was provided by a grant from the US Department of 249 Education NIDRR (grant number H133A130057, J.B., https://www.ed.gov/); and a 250 grant from the National Science Foundation (grant number IIS-1618784, J.B. and L.R., 251 https://www.nsf.gov/).
• dc.format.mimetype application/pdf
• dc.identifier.citation Rello L, Baeza-Yates R, Ali A, Bigham JP, Serra M. Predicting risk of dyslexia with an online gamified test. PLoS ONE. 2020;15(12):e0241687. DOI: 10.1371/journal.pone.0241687
• dc.identifier.doi http://dx.doi.org/10.1371/journal.pone.0241687
• dc.identifier.issn 1932-6203
• dc.identifier.uri http://hdl.handle.net/10230/46999
• dc.language.iso eng
• dc.publisher Public Library of Science (PLoS)
• dc.relation.ispartof PLoS ONE. 2020;15(12):e0241687
• dc.relation.isreferencedby https://doi.org/10.34740/kaggle/dsv/1617514
• dc.rights © 2020 Rello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
• 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 Phonology
• dc.subject.keyword Vision
• dc.subject.keyword Semantics
• dc.subject.keyword Machine learning
• dc.subject.keyword Working memory
• dc.subject.keyword Attention
• dc.subject.keyword Sytnax
• dc.title Predicting risk of dyslexia with an online gamified test
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion