Data mining in foreign language learning

Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work...

Descripción completa

Detalles Bibliográficos
Autores: Bravo-Agapito, Javier, Bonilla, Claire Frances, Seoane Pujol, Isaac
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad a Distancia de Madrid (UDIMA)
Repositorio:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
OAI Identifier:oai:udimundus.udima.es:20.500.12226/2427
Acceso en línea:http://hdl.handle.net/20.500.12226/2427
https://doi.org/10.1002/widm.1287
Access Level:acceso abierto
Palabra clave:Data mining, Foreign Language Learning, Educational Data Mining
id ES_5eaa7f208c166dffd4b3ab99c851bba6
oai_identifier_str oai:udimundus.udima.es:20.500.12226/2427
network_acronym_str ES
network_name_str España
repository_id_str
spelling Data mining in foreign language learningBravo-Agapito, JavierBonilla, Claire FrancesSeoane Pujol, IsaacData mining, Foreign Language Learning, Educational Data MiningEducational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m-learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential.2019-20Escuela de Ciencias Técnicas e Ingeniería(GI-20/4) Grupo de investigación de educación y tecnología2020info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12226/2427https://doi.org/10.1002/widm.1287reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madridinstname:Universidad a Distancia de Madrid (UDIMA)Españolinfo:eu-repo/semantics/openAccessoai:udimundus.udima.es:20.500.12226/24272026-06-02T12:44:31Z
dc.title.none.fl_str_mv Data mining in foreign language learning
title Data mining in foreign language learning
spellingShingle Data mining in foreign language learning
Bravo-Agapito, Javier
Data mining, Foreign Language Learning, Educational Data Mining
title_short Data mining in foreign language learning
title_full Data mining in foreign language learning
title_fullStr Data mining in foreign language learning
title_full_unstemmed Data mining in foreign language learning
title_sort Data mining in foreign language learning
dc.creator.none.fl_str_mv Bravo-Agapito, Javier
Bonilla, Claire Frances
Seoane Pujol, Isaac
author Bravo-Agapito, Javier
author_facet Bravo-Agapito, Javier
Bonilla, Claire Frances
Seoane Pujol, Isaac
author_role author
author2 Bonilla, Claire Frances
Seoane Pujol, Isaac
author2_role author
author
dc.subject.none.fl_str_mv Data mining, Foreign Language Learning, Educational Data Mining
topic Data mining, Foreign Language Learning, Educational Data Mining
description Educational data mining (EDM) combines the techniques of data mining with educational data in order to provide students, instructors, and researchers with knowledge that can benefit academic processes. Due to globalization, foreign language learning (FLL) has become increasingly important. This work seeks to gain insight as to how data mining (DM) is being used to benefit FLL. For this purpose, an advanced review of pertinent research published from 2012 to 2017 was performed. After applying our screening method, 208 papers were selected for the exhaustive analysis. This analysis was divided into four aspects: context (educational environments, educational level), number of items, DM methods, and DM applications. The results indicated that 54% of studies were conducted in traditional environments, while only 3% of studies were performed in an m-learning environment. In addition, 25 and 72% of the research was conducted in either a primary or secondary level, or in tertiary or adult level, respectively. Likewise, 76% of studies contained datasets of less than 1,000 items. The most utilized EDM methods were: factor analysis, regression, text mining, correlation mining, and causal DM. In addition, the studies analyzed showed that DM is mainly employed to predict the performance of students, to check learners' motivation, and to provide feedback for instructors. These results seem to indicate that although DM has much to offer the increasing number of language students, it is not being used to its full potential.
publishDate 2020
dc.date.none.fl_str_mv 2020
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12226/2427
https://doi.org/10.1002/widm.1287
url http://hdl.handle.net/20.500.12226/2427
https://doi.org/10.1002/widm.1287
dc.language.none.fl_str_mv Español
language_invalid_str_mv Español
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Escuela de Ciencias Técnicas e Ingeniería
(GI-20/4) Grupo de investigación de educación y tecnología
publisher.none.fl_str_mv Escuela de Ciencias Técnicas e Ingeniería
(GI-20/4) Grupo de investigación de educación y tecnología
dc.source.none.fl_str_mv reponame:udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
instname:Universidad a Distancia de Madrid (UDIMA)
instname_str Universidad a Distancia de Madrid (UDIMA)
reponame_str udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
collection udiMundus. Repositorio Institucional de la Universidad a Distancia de Madrid
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869409145030967296
score 15,811543

Similares en LA Referencia