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dc.contributor.authorEscobar Acevedo, Adelina
dc.contributor.authorGuerrero García, Josefina
dc.date.accessioned2024-09-16T21:29:46Z
dc.date.available2024-09-16T21:29:46Z
dc.date.issued2022-04-18
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26580
dc.description.abstractDurante la adquisición de un idioma extranjero, la lectura representa una de las oportunidades de acercamiento al lenguaje. Sin embargo, los textos inadecuados pueden desencadenar una experiencia contraproducente para un estudiante, por ello, en los cursos regulares, los docentes utilizan su experiencia o la de un equipo editorial para seleccionar las lecturas. En un sistema automático como en un Sistema Tutor Inteligente, es prioritario realizar recomendaciones adecuadas al perfil del alumno. No basta conocer el nivel de idioma del texto, El presente trabajo aplica herramientas para clasificar una muestra de textos extraídos del corpus OneStopEnglish conforme al Marco Común de Referencia Europeo, crea grupos temáticos con análisis semántico latente (LSA), y aplica tres métricas populares de lecturabilidad como un referente para recomendar textos a los estudiantes.spa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4484/3612spa
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/282spa
dc.sourceVol. 23 Núm. 1 (2022): Revista Colombiana de Computación (Enero-Junio); 53-60spa
dc.subjectSelección de materialesspa
dc.subjectMétricas de lecturabilidadspa
dc.subjectComprensión lectoraspa
dc.subjectSistemas Tutores Inteligentesspa
dc.titleConstrucción de contenido para un Sistema Tutor Inteligente en idiomas: un estudio piloto con el corpus OneStopEnglishspa
dc.title.translatedContent Construction for an Intelligent Tutor System in languages: a pilot study on the OneStopEnglish corpuseng
dc.type.driverinfo:eu-repo/semantics/article
dc.type.localArtículospa
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.subject.keywordsMaterial recommendationeng
dc.subject.keywordsReadability metricseng
dc.subject.keywordsReading Comprehensioneng
dc.subject.keywordsIntelligent Tutor Systemseng
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
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dc.contributor.orcidEscobar Acevedo, Adelina [0000-0003-0574-0932]spa
dc.contributor.orcidGuerrero García, Josefina [0000-0002-3393-610X]spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.description.abstractenglishDuring foreign language acquisition, reading represents one of the opportunities to get closer to the language. However, inappropriate texts can cause students to have a negative experience; thus, in regular courses, teachers use their experience or an editorial team to select the readings. In an automatic system, as in an Intelligent Tutor System, making recommendations appropriate to the student's profile is a priority. It is not enough to know the language level of the text. This work uses tools to classify a sample of texts from the OneStopEnglish corpus according to the Common European Framework of Reference for Languages. We create thematic groups based on Latent Semantic Analysis (LSA) and use three popular metrics of readability as a guide to suggest texts to students.eng
dc.identifier.doihttps://doi.org/10.29375/25392115.4484
dc.type.redcolhttp://purl.org/redcol/resource_type/ART
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa


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