Dytective
| Autor: | Nancy Cushen White, Abdullah Ali, Luz Rello, Kristin Williams, Jeffrey P. Bigham |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
School age child
05 social sciences Dyslexia Early detection Spanish speaking 02 engineering and technology medicine.disease Developmental psychology Global population 020204 information systems Drop out Scale (social sciences) 0202 electrical engineering electronic engineering information engineering medicine 0501 psychology and cognitive sciences Psychology 050107 human factors |
| Zdroj: | W4A |
| DOI: | 10.1145/2899475.2899491 |
| Popis: | At least 10% of the global population has dyslexia. In the United States and Spain, dyslexia is associated with a large percentage of school drop out. Current methods to detect risk of dyslexia are language specific, expensive, or do not scale well because they require a professional or extensive equipment. A central challenge to detecting dyslexia is handling its differing manifestations across languages. To address this, we designed a browser-based game, Dytective, to detect risk of dyslexia across the English and Spanish languages. Dytective consists of linguistic tasks informed by analysis of common errors made by persons with dyslexia. To evaluate Dytective, we conducted a user study with 60 English and Spanish speaking children between 7 and 12 years old. We found children with and without dyslexia differed significantly in their performance on the game. Our results suggest that Dytective is able to differentiate school age children with and without dyslexia in both English and Spanish speakers. |
| Databáze: | OpenAIRE |
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