Construcción de contenido para un Sistema Tutor Inteligente en idiomas: un estudio piloto con el corpus OneStopEnglish

• Escobar-Acevedo, Adelina ^[1] ; Guerrero-García, Josefina ^[1]
  1. [1] Benemérita Universidad Autónoma de Puebla

     Benemérita Universidad Autónoma de Puebla

     México

     
• Localización: Revista Colombiana de Computación, ISSN 1657-2831, ISSN-e 2539-2115, Vol. 23, Nº. 1 (Enero-Junio), 2022, págs. 53-60
• Idioma: español
• DOI: 10.29375/25392115.4484
• Títulos paralelos:
  • Content Construction for an Intelligent Tutor System in languages: a pilot study on the OneStopEnglish corpus
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Durante 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.

  • English

    During 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.

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