Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review

As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to perso...

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Detalles Bibliográficos
Autores: Narimani, Amir, Barbera, Elena
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universitat Oberta de Catalunya (UOC)
Repositorio:O2, repositorio institucional de la UOC
OAI Identifier:oai:openaccess.uoc.edu:10609/151541
Acceso en línea:http://hdl.handle.net/10609/151541
https://doi.org/10.19173/irrodl.v25i1.7419
Access Level:acceso abierto
Palabra clave:online learning
personalization
course recommender systems
course features
learner profiles
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spelling Extracting course features and learner profiling for course recommendation systems: a comprehensive literature reviewNarimani, AmirBarbera, Elenaonline learningpersonalizationcourse recommender systemscourse featureslearner profilesAs education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.International Review of Research in Open and Distance Learning202420242024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10609/151541https://doi.org/10.19173/irrodl.v25i1.7419reponame:O2, repositorio institucional de la UOCinstname:Universitat Oberta de Catalunya (UOC)InglésThe International Review of Research in Open and Distributed Learning, 2024, 25(1)https://www.irrodl.org/index.php/irrodl/article/view/7419CC BYhttp://creativecommons.org/licenses/by/3.0/es/info:eu-repo/semantics/openAccessoai:openaccess.uoc.edu:10609/1515412026-05-28T12:42:01Z
dc.title.none.fl_str_mv Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
title Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
spellingShingle Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
Narimani, Amir
online learning
personalization
course recommender systems
course features
learner profiles
title_short Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
title_full Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
title_fullStr Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
title_full_unstemmed Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
title_sort Extracting course features and learner profiling for course recommendation systems: a comprehensive literature review
dc.creator.none.fl_str_mv Narimani, Amir
Barbera, Elena
author Narimani, Amir
author_facet Narimani, Amir
Barbera, Elena
author_role author
author2 Barbera, Elena
author2_role author
dc.subject.none.fl_str_mv online learning
personalization
course recommender systems
course features
learner profiles
topic online learning
personalization
course recommender systems
course features
learner profiles
description As education has evolved towards online learning, the availability of learning materials has expanded and consequently, learners’ behavior in choosing resources has changed. The need to offer personalized learning experiences and content has never been greater. Research has explored methods to personalize learning paths and match learning materials with learners’ profiles. Course recommendation systems have emerged as a solution to help learners select courses that suit their interests and aptitude. A comprehensive review study was required to explore the implementation of course recommender systems, with the specifics of courses and learners as the main focal points. This study provided a framework to explain and categorize data sources for course feature extraction, and described the information sources used in previous research to model learner profiles for course recommendations. This review covered articles published between 2015 and 2022 in the repositories most relevant to education and computer science. It revealed increased attention paid to combining course features from different sources. The creation of multi-dimensional learner profiles using multiple learner characteristics and implementing machine-learning-based recommenders has recently gained momentum. As well, a lack of focus on learners’ micro-behaviors and learning actions to create precise models was noted in the literature. Conclusions about recent course recommendation systems development are also discussed.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10609/151541
https://doi.org/10.19173/irrodl.v25i1.7419
url http://hdl.handle.net/10609/151541
https://doi.org/10.19173/irrodl.v25i1.7419
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv The International Review of Research in Open and Distributed Learning, 2024, 25(1)
https://www.irrodl.org/index.php/irrodl/article/view/7419
dc.rights.none.fl_str_mv CC BY
http://creativecommons.org/licenses/by/3.0/es/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv CC BY
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv International Review of Research in Open and Distance Learning
publisher.none.fl_str_mv International Review of Research in Open and Distance Learning
dc.source.none.fl_str_mv reponame:O2, repositorio institucional de la UOC
instname:Universitat Oberta de Catalunya (UOC)
instname_str Universitat Oberta de Catalunya (UOC)
reponame_str O2, repositorio institucional de la UOC
collection O2, repositorio institucional de la UOC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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