english | german
The aim of our web-game called MusVis (see Video) is to measure the reaction of children with and without dyslexia while playing, in order to find differences on their behavior. Dyslexia is a specific learning disorder that affects from 5% to 15% of the world population.
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.
Our approach aims to screen dyslexia with indicators that do not require linguistic knowledge. These indicators are probably not as strong or visible as the reading and spelling mistakes of children with dyslexia. Therefore, we consider our results (highest accuracy of 0.74 and highest F1-scores of 0.75) for German with Random Forest and (highest accuracy of 0.69) for Spanish with Extra Trees as a promising way to predict dyslexia using language-independent auditory and visual content for pre-readers.
Our approach can optimize resources for detecting and treating dyslexia, however, it would need at the beginning more personnel to screen many more children at a young age to enlarge our training data. As children with dyslexia need around two years to compensate their difficulties, our approach could help to decrease school failure, late treatment, and most importantly, to reduce suffering for children and parents.
The main advantage of our language-independent content approach is that has the potential to screen pre-readers in the near future. Indeed, we aim to collect more data with younger children to improve our results.
The full paper is available here: Screening risk of dyslexia through a web-game using language-independent content and machine learning
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