Browsing by Author "Baeza Yates, Ricardo"

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    A Universal Screening Tool for Dyslexia by a Web-Game and Machine Learning
    (Frontiers, 2022-01-03) Rello, Luz; Rauschenberge, Maria; Baeza Yates, Ricardo; https://ror.org/02jjdwm75
    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.
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    Predicting risk of dyslexia with an online gamified test
    (Public Library of Science, 2020) Baeza Yates, Ricardo; Ali, Abdullah; Bigham, Jeffrey ; Serra, Miquel; Rello, Luz; National Science Foundation; Universidad San Jorge; https://ror.org/02jjdwm75
    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. © 2020 Rello et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use,distribution,and reproduction in any medium,provided the original author and source are credited.