Predicting risk of dyslexia with an online gamified test

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...

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Detalles Bibliográficos
Autores: Rello, Luz, 1984-, Baeza Yates, Ricardo, Ali, Abdullah, Bigham, Jeffrey P., Serra Burriel, Miquel
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universitat Pompeu Fabra
Repositorio:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/46999
Acceso en línea:http://hdl.handle.net/10230/46999
http://dx.doi.org/10.1371/journal.pone.0241687
Access Level:acceso abierto
Palabra clave:Dyslexia
Phonology
Vision
Semantics
Machine learning
Working memory
Attention
Sytnax
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repository_id_str
spelling Predicting risk of dyslexia with an online gamified testRello, Luz, 1984-Baeza Yates, RicardoAli, AbdullahBigham, Jeffrey P.Serra Burriel, MiquelDyslexiaPhonologyVisionSemanticsMachine learningWorking memoryAttentionSytnaxDyslexia 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.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/).Public Library of Science (PLoS)202120212020info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/46999http://dx.doi.org/10.1371/journal.pone.0241687reponame:Repositorio Digital de la UPFinstname:Universitat Pompeu FabraInglésPLoS ONE. 2020;15(12):e0241687https://doi.org/10.34740/kaggle/dsv/1617514© 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.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:repositori.upf.edu:10230/469992026-06-12T07:21:37Z
dc.title.none.fl_str_mv Predicting risk of dyslexia with an online gamified test
title Predicting risk of dyslexia with an online gamified test
spellingShingle Predicting risk of dyslexia with an online gamified test
Rello, Luz, 1984-
Dyslexia
Phonology
Vision
Semantics
Machine learning
Working memory
Attention
Sytnax
title_short Predicting risk of dyslexia with an online gamified test
title_full Predicting risk of dyslexia with an online gamified test
title_fullStr Predicting risk of dyslexia with an online gamified test
title_full_unstemmed Predicting risk of dyslexia with an online gamified test
title_sort Predicting risk of dyslexia with an online gamified test
dc.creator.none.fl_str_mv Rello, Luz, 1984-
Baeza Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra Burriel, Miquel
author Rello, Luz, 1984-
author_facet Rello, Luz, 1984-
Baeza Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra Burriel, Miquel
author_role author
author2 Baeza Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra Burriel, Miquel
author2_role author
author
author
author
dc.subject.none.fl_str_mv Dyslexia
Phonology
Vision
Semantics
Machine learning
Working memory
Attention
Sytnax
topic Dyslexia
Phonology
Vision
Semantics
Machine learning
Working memory
Attention
Sytnax
description 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.
publishDate 2020
dc.date.none.fl_str_mv 2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/46999
http://dx.doi.org/10.1371/journal.pone.0241687
url http://hdl.handle.net/10230/46999
http://dx.doi.org/10.1371/journal.pone.0241687
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PLoS ONE. 2020;15(12):e0241687
https://doi.org/10.34740/kaggle/dsv/1617514
dc.rights.none.fl_str_mv https://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Public Library of Science (PLoS)
publisher.none.fl_str_mv Public Library of Science (PLoS)
dc.source.none.fl_str_mv reponame:Repositorio Digital de la UPF
instname:Universitat Pompeu Fabra
instname_str Universitat Pompeu Fabra
reponame_str Repositorio Digital de la UPF
collection Repositorio Digital de la UPF
repository.name.fl_str_mv
repository.mail.fl_str_mv
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