Name
Title | ||
|---|---|---|
Screening risk of dyslexia through a web-game using language-independent content and machine learning |
Abstract | ||
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Children with dyslexia are often diagnosed after they fail school even if dyslexia is not related to general intelligence. In this work, 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. To the best of our knowledge this is the first time that risk of dyslexia is screened using a language-independent content web-based game and machine-learning. 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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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3371300.3383342 | W4A '20: 17th Web for All Conference
Taipei
Taiwan
April, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7056-1 | 0 |
PageRank | References | Authors |
0.34 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maria Rauschenberger | 1 | 31 | 7.49 |
| Ricardo Baeza-Yates | 2 | 6173 | 635.97 |
| Luz Rello | 3 | 333 | 39.12 |