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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 principales: Rello, Luz, Baeza-Yates, Ricardo, Ali, Abdullah, Bigham, Jeffrey P., Serra, Miquel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710040/
https://www.ncbi.nlm.nih.gov/pubmed/33264301
http://dx.doi.org/10.1371/journal.pone.0241687
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author Rello, Luz
Baeza-Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra, Miquel
author_facet Rello, Luz
Baeza-Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra, Miquel
author_sort Rello, Luz
collection PubMed
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.
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spelling pubmed-77100402020-12-03 Predicting risk of dyslexia with an online gamified test Rello, Luz Baeza-Yates, Ricardo Ali, Abdullah Bigham, Jeffrey P. Serra, Miquel PLoS One Research Article 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. Public Library of Science 2020-12-02 /pmc/articles/PMC7710040/ /pubmed/33264301 http://dx.doi.org/10.1371/journal.pone.0241687 Text en © 2020 Rello et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Rello, Luz
Baeza-Yates, Ricardo
Ali, Abdullah
Bigham, Jeffrey P.
Serra, Miquel
Predicting risk of dyslexia with an online gamified test
title 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_short Predicting risk of dyslexia with an online gamified test
title_sort predicting risk of dyslexia with an online gamified test
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7710040/
https://www.ncbi.nlm.nih.gov/pubmed/33264301
http://dx.doi.org/10.1371/journal.pone.0241687
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