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Using serious games to screen dyslexia has been a suc- cessful approach for English, German and Spanish. In a pilot study with a desktop game, we addressed pre-readers screening, that is, younger children who have not acquired reading or writing skills. Based on our results, we have redesigned the game content and new interactions with visual and musical cues. Hence, here we present a tablet game, DGames, which has the potential to predict dyslexia in pre-readers. This could contribute to around 10% of the population that is affected by dyslexia, as children will gain more time to learn to cope with the challenges of learning how to read and write.
Nowadays, being excluded from the web is a huge disadvantage. People with dyslexia have, despite their general intelligence, difficulties for reading and writing through their whole life. Therefore, web technologies can help people with dyslexia to improve their reading and writing experience on the web. This chapter introduces the main technologies and many examples of tools that support a person with dyslexia in processing information on the web, either in assistive applications for reading and writing as well as using web applications/games for dyslexia screening and intervention.
Luz Rello
Enrique Romero- Maria Rauschenberger
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Nancy Cushen White
More than 10% of the population has dyslexia, and most are di- agnosed only after they fail in school. This work seeks to change this through early detection via machine learning models that pre- dict dyslexia by observing how people interact with a linguistic computer-based game.We designed items of the game taking into account (i) the empirical linguistic analysis of the errors that peo- ple with dyslexia make, and (ii) specific cognitive skills related to dyslexia: Language Skills, Working Memory, Executive Functions, and Perceptual Processes. . Using measures derived from the game, we conducted an experiment with 267 children and adults in order to train a statistical model that predicts readers with and without dyslexia using measures derived from the game. The model was trained and evaluated in a 10-fold cross experiment, reaching 84.62% accuracy using the most informative features.
CCS
