Dytective: Diagnosing Risk of Dyslexia with a Game

Abstract

More than 10% of the population has dyslexia, and most are diagnosed only after they fail in school. This work seeks to change this through scalable early detection via machine learning models that predict reading and writing difficulties by watching how people interact with a linguistic web-based game: Dytective. The design of Dytective is based on (i) the empirical linguistic analysis of the errors that people with dyslexia make, (ii) principles of language acquisition, and (iii) specific linguistic skills related to dyslexia. Experiments with 243 children and adults (95 with diagnosed dyslexia) revealed differences in how people with dyslexia read and write. We trained a machine learning model that was able to predict dyslexia with 83% accuracy in a held-out test set with 100 participants. Currently, we are working with schools to put our approach into practice at scale to reduce school failure as a primary way dyslexia is diagnosed.

Towards Language Independent Detection of Dyslexia with a Web-based Game

“…Therefore, we designed our game with content that is known to be difficult to differentiate for children with dyslexia to measure the errors and the duration. Nevertheless, from previous literature we knew that children with dyslexia do not make more mistakes in games than the control group [28]. We can confirm that misses did not reveal significant differences for German or Spanish either.…”

“…We decided to use a laptop or desktop computer for two reasons: (1) From prior game evaluation [6,28] we know that readers are able to interact with the device and (2) these devices are still more available than tablets [13].…”

“…These are common indicators for detecting a person with dyslexia. Other indicators for detecting dyslexia relate to linguistic skills, e.g., prosodic or phonological awareness [9], differences in reading and spelling error rates of people with and without dyslexia [3,31], and differences in game measures derived from phonological awareness, letter recognition or word recognition [28]. These and other language related indicators have been used in various software for screening, prediction or detection of dyslexia (we use this three terms indistinguishably).…”

“…These and other language related indicators have been used in various software for screening, prediction or detection of dyslexia (we use this three terms indistinguishably). Examples of software for detecting dyslexia in English are Lexercise Screener [15], Nessy [19] and Dytective for both English [29] and Spanish [28].…”

Whale Behavior based Rule Optimization on Paraconsistent Neutrosophic Classification Model for Prediction of Dyslexia

“…Luz Rello et al [4] performs the early detection of dyslexia using machine learning approaches which observers the interactivity of people with a web based linguistic game known as dytective. They trained the model with 100 participants and produces 83% accuracy in prediction of dyslexia.…”