Mostra el registre d'ítem simple
Screening dyslexia for English using HCI measures and machine learning
dc.contributor.author | Rello, Luz |
dc.contributor.author | Romero Merino, Enrique |
dc.contributor.author | Rauschenberger, Maria |
dc.contributor.author | Ali, Abdullah |
dc.contributor.author | Williams, Kristin |
dc.contributor.author | Bigham, Jeffrey P. |
dc.contributor.author | White, Nancy Cushen |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2018-11-12T10:05:06Z |
dc.date.available | 2018-11-12T10:05:06Z |
dc.date.issued | 2018 |
dc.identifier.citation | Rello, 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.isbn | 978-1-4503-6493-5 |
dc.identifier.uri | http://hdl.handle.net/2117/123915 |
dc.description.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 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.extent | 5 p. |
dc.language.iso | eng |
dc.publisher | Association for Computing Machinery (ACM) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic |
dc.subject.lcsh | Machine learning |
dc.subject.lcsh | Dyslexia |
dc.subject.other | Diagnosis |
dc.subject.other | Early detection |
dc.subject.other | Linguistics |
dc.subject.other | Screening |
dc.subject.other | Serious games |
dc.title | Screening dyslexia for English using HCI measures and machine learning |
dc.type | Conference report |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Dislèxia |
dc.identifier.doi | 10.1145/3194658.3194675 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://dl.acm.org/citation.cfm?id=3194675 |
dc.rights.access | Open Access |
local.identifier.drac | 23438279 |
dc.description.version | Postprint (author's final draft) |
local.citation.author | Rello, L.; Romero, E.; Rauschenberger, M.; Ali, A.; Williams, K.; Bigham, J.; White, N. |
local.citation.contributor | International Conference on Digital Health |
local.citation.pubplace | New York |
local.citation.publicationName | Proceedings of the 2018 International Conference on Digital Health, DH 2018: Lyon, France, April 23-26, 2018 |
local.citation.startingPage | 80 |
local.citation.endingPage | 84 |