Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty

Araujo, Lourdes, López-Ostenero, Fernando, Martínez-Romo, Juan y Plaza, Laura . (2020) Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty. IEEE Access

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Título Deep-Learning Approach to Educational Text Mining and Application to the Analysis of Topics’ Difficulty
Autor(es) Araujo, Lourdes
López-Ostenero, Fernando
Martínez-Romo, Juan
Plaza, Laura
Materia(s) Ingeniería Informática
Abstract Learning analytics has emerged as a promising tool for optimizing the learning experience and results, especially in online educational environments. An important challenge in this area is identifying the most difficult topics for students in a subject, which is of great use to improve the quality of teaching by devoting more effort to those topics of greater difficulty, assigning them more time, resources and materials. We have approached the problem by means of natural language processing techniques. In particular, we propose a solution based on a deep learning model that automatically extracts the main topics that are covered in educational documents. This model is next applied to the problem of identifying the most difficult topics for students in a subject related to the study of algorithms and data structures in a Computer Science degree. Our results show that our topic identification model presents very high accuracy (around 90 percent) and may be efficiently used in learning analytics applications, such as the identification and understanding of what makes the learning of a subject difficult. An exhaustive analysis of the case study has also revealed that there are indeed topics that are consistently more difficult for most students, and also that the perception of difficulty in students and teachers does not always coincide with the actual difficulty indicated by the data, preventing to pay adequate attention to the most challenging topics.
Palabras clave Deep learning
text mining
learning analytics
teaching of algorithms
challenging topics
Editor(es) Institute of Electrical and Electronics Engineers
Fecha 2020-12-02
Formato application/pdf
Identificador bibliuned:DptoLSI-ETSI-Articulos-Laraujo-0004
http://e-spacio.uned.es/fez/view/bibliuned:DptoLSI-ETSI-Articulos-Laraujo-0004
DOI - identifier http://doi.org/10.1109/ACCESS.2020.3042099
ISSN - identifier 2169-3536
Nombre de la revista IEEE Access
Número de Volumen 18
Publicado en la Revista IEEE Access
Idioma eng
Versión de la publicación publishedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales La versión registrada de este artículo, publicado por primera vez en IEEE Access (2019) 18, está disponible en línea en el sitio web del editor: Institute of Electrical and Electronics Engineers, http://doi.org/10.1109/ACCESS.2020.3042099
Notas adicionales The registered version of this article, first published in IEEE Access (2019) 18, is available online at the publisher's website: Institute of Electrical and Electronics Engineers, http://doi.org/10.1109/ACCESS.2020.3042099

 
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Creado: Fri, 22 Mar 2024, 23:05:44 CET