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dc.contributor.authorRello, Luz
dc.contributor.authorRomero Merino, Enrique
dc.contributor.authorRauschenberger, Maria
dc.contributor.authorAli, Abdullah
dc.contributor.authorWilliams, Kristin
dc.contributor.authorBigham, Jeffrey P.
dc.contributor.authorWhite, Nancy Cushen
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.date.accessioned2018-11-12T10:05:06Z
dc.date.available2018-11-12T10:05:06Z
dc.date.issued2018
dc.identifier.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.
dc.identifier.isbn978-1-4503-6493-5
dc.identifier.urihttp://hdl.handle.net/2117/123915
dc.description.abstractMore 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.
dc.format.extent5 p.
dc.language.isoeng
dc.publisherAssociation for Computing Machinery (ACM)
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
dc.subject.lcshMachine learning
dc.subject.lcshDyslexia
dc.subject.otherDiagnosis
dc.subject.otherEarly detection
dc.subject.otherLinguistics
dc.subject.otherScreening
dc.subject.otherSerious games
dc.titleScreening dyslexia for English using HCI measures and machine learning
dc.typeConference report
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacDislèxia
dc.identifier.doi10.1145/3194658.3194675
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://dl.acm.org/citation.cfm?id=3194675
dc.rights.accessOpen Access
local.identifier.drac23438279
dc.description.versionPostprint (author's final draft)
local.citation.authorRello, L.; Romero, E.; Rauschenberger, M.; Ali, A.; Williams, K.; Bigham, J.; White, N.
local.citation.contributorInternational Conference on Digital Health
local.citation.pubplaceNew York
local.citation.publicationNameProceedings of the 2018 International Conference on Digital Health, DH 2018: Lyon, France, April 23-26, 2018
local.citation.startingPage80
local.citation.endingPage84


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