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dc.contributor.authorAbou-Khalil, Victoriaen
dc.contributor.authorBrendanen
dc.contributor.authorFlanaganen
dc.contributor.authorOgata, Hiroakien
dc.contributor.alternative緒方, 広明ja
dc.date.accessioned2018-08-02T04:37:08Z-
dc.date.available2018-08-02T04:37:08Z-
dc.date.issued2018-
dc.identifier.urihttp://hdl.handle.net/2433/233070-
dc.descriptionLAK’18: 8th International Learning Analytics and Knowledge (LAK) Conference, SMC Conference & Function Centre in Sydney, Australia on March 5–9, 2018.en
dc.description.abstractFalse friends are words in two languages that look or sound similar but differ significantly in meaning in some or all contexts. False friends are confusing for language students and could result in frustration and communication problems. This paper proposes a method to diagnose and prevent false friends mistakes based on students’ past learned words, current location and time. The proposed method uses records from the SCROLL system (System for Capturing and Reminding Of Learning Log) to analyze the previous activity of students. We assume that the past activity of a student can be used to predict the meaning intended by the student when looking up a polysemous word. The identification of the intended meaning in the student's current context is then used to provide the student with the appropriate translation, warnings and quizzes, possibly improving the learning process and avoiding false friends future mistakes.en
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherAssociation for Computing Machinery (ACM)en
dc.rightsThis work is published under the terms of the Creative Commons Attribution- Noncommercial-ShareAlike 3.0 Australia Licence. Under this Licence you are free to: Share - copy and redistribute the material in any medium or format.en
dc.subjectLearning Analyticsen
dc.subjectUbiquitous learningen
dc.subjectFalse Friendsen
dc.subjectComputer Supported Language Learningen
dc.titleLearning false friends across contextsen
dc.typeconference paper-
dc.type.niitypeConference Paper-
dc.identifier.jtitleCompanion Proceedings 8th International Conference on Learning Analytics & Knowledge (LAK18)-
dc.textversionpublisher-
dc.addressGraduate School of Informatics, Kyoto Universityen
dc.addressAcademic Center for Computing and Media studies, Kyoto Universityen
dc.addressAcademic Center for Computing and Media studies, Kyoto Universityen
dc.relation.urlhttps://www.researchgate.net/publication/324690419_Companion_Proceedings_of_the_8th_International_Conference_on_Learning_Analytics_Knowledge_LAK'18-
dcterms.accessRightsopen access-
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