A generic architecture of an affective recommender system for e-learning environments

Personalization of suggestions of contents plays a key role in adaptive virtual learning environments. Good recommendations can raise the interest of students in the learning process, while, on the other hand, bad recommendations can have catastrophic results for the learning process. The affective...

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Detalles Bibliográficos
Autores: Salazar, Juan, Aguilar, Jose, Monsalve, Julian, Montoya, Edwin
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:IMDEA Networks Institute
Repositorio:IMDEA Networks Institute Digital Repository
Idioma:inglés
OAI Identifier:oai:dspace.networks.imdea.org:20.500.12761/1738
Acceso en línea:https://hdl.handle.net/20.500.12761/1738
https://dx.doi.org/10.1007/s10209-023-01024-8
Access Level:acceso abierto
Palabra clave:Affective Recommendation Systems
Virtual Learning Environments
Emotion Recognition
Sentiment Analysis
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oai_identifier_str oai:dspace.networks.imdea.org:20.500.12761/1738
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repository_id_str
spelling A generic architecture of an affective recommender system for e-learning environmentsSalazar, JuanAguilar, JoseMonsalve, JulianMontoya, EdwinAffective Recommendation SystemsVirtual Learning EnvironmentsEmotion RecognitionSentiment AnalysisPersonalization of suggestions of contents plays a key role in adaptive virtual learning environments. Good recommendations can raise the interest of students in the learning process, while, on the other hand, bad recommendations can have catastrophic results for the learning process. The affective state of the student is a very influential factor in the learning process. In this work, a generic architecture of an affective recommender system for e-learning environments is developed, to serve as a guide for future implementations of this kind of recommender system. Here, the affective characteristics of students are represented by their personalities, learning styles, emotional states, and expertise levels. Thus, the main contribution is the proposition of a generic architecture of an affective recommendation system for the educational field. The architecture is completely modular, which gives it great flexibility because the emotion engine is separated from the personal characteristics engine, and is not based on specific models of emotions. This work finishes with examples of use cases of the architecture. According to the results in these examples, our architecture is capable of analyzing the polarity of academic documents based on their content, determining the personal characteristics of students (including their emotions), and from there, recommending learning resources to students considering emotions as the main element of the process.TRUEpubSpringer20232023-08-30journal articlehttp://purl.org/coar/resource_type/c_6501AOhttp://purl.org/coar/version/c_b1a7d7d4d402bcceinfo:eu-repo/semantics/articlehttps://hdl.handle.net/20.500.12761/1738https://dx.doi.org/10.1007/s10209-023-01024-8reponame:IMDEA Networks Institute Digital Repositoryinstname:IMDEA Networks InstituteInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:dspace.networks.imdea.org:20.500.12761/17382026-06-06T12:35:51Z
dc.title.none.fl_str_mv A generic architecture of an affective recommender system for e-learning environments
title A generic architecture of an affective recommender system for e-learning environments
spellingShingle A generic architecture of an affective recommender system for e-learning environments
Salazar, Juan
Affective Recommendation Systems
Virtual Learning Environments
Emotion Recognition
Sentiment Analysis
title_short A generic architecture of an affective recommender system for e-learning environments
title_full A generic architecture of an affective recommender system for e-learning environments
title_fullStr A generic architecture of an affective recommender system for e-learning environments
title_full_unstemmed A generic architecture of an affective recommender system for e-learning environments
title_sort A generic architecture of an affective recommender system for e-learning environments
dc.creator.none.fl_str_mv Salazar, Juan
Aguilar, Jose
Monsalve, Julian
Montoya, Edwin
author Salazar, Juan
author_facet Salazar, Juan
Aguilar, Jose
Monsalve, Julian
Montoya, Edwin
author_role author
author2 Aguilar, Jose
Monsalve, Julian
Montoya, Edwin
author2_role author
author
author
dc.subject.none.fl_str_mv Affective Recommendation Systems
Virtual Learning Environments
Emotion Recognition
Sentiment Analysis
topic Affective Recommendation Systems
Virtual Learning Environments
Emotion Recognition
Sentiment Analysis
description Personalization of suggestions of contents plays a key role in adaptive virtual learning environments. Good recommendations can raise the interest of students in the learning process, while, on the other hand, bad recommendations can have catastrophic results for the learning process. The affective state of the student is a very influential factor in the learning process. In this work, a generic architecture of an affective recommender system for e-learning environments is developed, to serve as a guide for future implementations of this kind of recommender system. Here, the affective characteristics of students are represented by their personalities, learning styles, emotional states, and expertise levels. Thus, the main contribution is the proposition of a generic architecture of an affective recommendation system for the educational field. The architecture is completely modular, which gives it great flexibility because the emotion engine is separated from the personal characteristics engine, and is not based on specific models of emotions. This work finishes with examples of use cases of the architecture. According to the results in these examples, our architecture is capable of analyzing the polarity of academic documents based on their content, determining the personal characteristics of students (including their emotions), and from there, recommending learning resources to students considering emotions as the main element of the process.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-08-30
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AO
http://purl.org/coar/version/c_b1a7d7d4d402bcce
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/20.500.12761/1738
https://dx.doi.org/10.1007/s10209-023-01024-8
url https://hdl.handle.net/20.500.12761/1738
https://dx.doi.org/10.1007/s10209-023-01024-8
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:IMDEA Networks Institute Digital Repository
instname:IMDEA Networks Institute
instname_str IMDEA Networks Institute
reponame_str IMDEA Networks Institute Digital Repository
collection IMDEA Networks Institute Digital Repository
repository.name.fl_str_mv
repository.mail.fl_str_mv
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