Screening dyslexia for English using HCI measures and machine learning
Document typeConference report
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
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 early detection via machine learning models that predict 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 people 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.
CitationRello, L., Romero, E., Rauschenberger, M., Ali, A., Williams, K., Bigham, J., White, N. Screening dyslexia for English using HCI measures and machine learning. A: International Conference on Digital Health. "Proceedings of the 2018 International Conference on Digital Health, DH 2018: Lyon, France, April 23-26, 2018". New York: Association for Computing Machinery (ACM), 2018, p. 80-84.