A comprehensive review of multimodal analysis in education

Multimodal learning analytics (MMLA) has become a prominent approach for capturing the complexity of learning by integrating diverse data sources such as video, audio, physiological signals, and digital interactions. This comprehensive review synthesises findings from 177 peer-reviewed studies to ex...

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Autores: Guerrero Sosa, Jared David Tadeo, Menéndez , Víctor Hugo, Romero Chicharro, Francisco Pascual, Montoro Montarroso, Andrés, Olivas Varela, José Ángel, Serrano-Guerrero, Jesús
Tipo de recurso: artículo
Fecha de publicación:2025
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/43686
Acceso en línea:https://doi.org/10.3390/app15115896
https://hdl.handle.net/10578/43686
Access Level:acceso abierto
Palabra clave:Educational data mining
Learning analytics
Multimodal feature extraction
Multimodal learning analytics
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spelling A comprehensive review of multimodal analysis in educationGuerrero Sosa, Jared David TadeoMenéndez , Víctor HugoRomero Chicharro, Francisco PascualMontoro Montarroso, AndrésOlivas Varela, José ÁngelSerrano-Guerrero, JesúsEducational data miningLearning analyticsMultimodal feature extractionMultimodal learning analyticsMultimodal learning analytics (MMLA) has become a prominent approach for capturing the complexity of learning by integrating diverse data sources such as video, audio, physiological signals, and digital interactions. This comprehensive review synthesises findings from 177 peer-reviewed studies to examine the foundations, methodologies, tools, and applications of MMLA in education. It provides a detailed analysis of data collection modalities, feature extraction pipelines, modelling techniques—including machine learning, deep learning, and fusion strategies—and software frameworks used across various educational settings. Applications are categorised by pedagogical goals, including engagement monitoring, collaborative learning, simulation-based environments, and inclusive education. The review identifies key challenges, such as data synchronisation, model interpretability, ethical concerns, and scalability barriers. It concludes by outlining future research directions, with emphasis on real-world deployment, longitudinal studies, explainable artificial intelligence, emerging modalities, and cross-cultural validation. This work aims to consolidate current knowledge, address gaps in practice, and offer practical guidance for researchers and practitioners advancing multimodal approaches in education.MDPI202520252025info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://doi.org/10.3390/app15115896https://hdl.handle.net/10578/43686Applied sciences, 2025, 15, 5896reponame:RUIdeRA. Repositorio Institucional de la UCLMinstname:Universidad de Castilla-La ManchaInglésPID2019-104735RB-C42 (ERA/ERDF, EU)PLEC2021-007681info:eu-repo/semantics/openAccessoai:ruidera.uclm.es:10578/436862026-05-27T07:36:41Z
dc.title.none.fl_str_mv A comprehensive review of multimodal analysis in education
title A comprehensive review of multimodal analysis in education
spellingShingle A comprehensive review of multimodal analysis in education
Guerrero Sosa, Jared David Tadeo
Educational data mining
Learning analytics
Multimodal feature extraction
Multimodal learning analytics
title_short A comprehensive review of multimodal analysis in education
title_full A comprehensive review of multimodal analysis in education
title_fullStr A comprehensive review of multimodal analysis in education
title_full_unstemmed A comprehensive review of multimodal analysis in education
title_sort A comprehensive review of multimodal analysis in education
dc.creator.none.fl_str_mv Guerrero Sosa, Jared David Tadeo
Menéndez , Víctor Hugo
Romero Chicharro, Francisco Pascual
Montoro Montarroso, Andrés
Olivas Varela, José Ángel
Serrano-Guerrero, Jesús
author Guerrero Sosa, Jared David Tadeo
author_facet Guerrero Sosa, Jared David Tadeo
Menéndez , Víctor Hugo
Romero Chicharro, Francisco Pascual
Montoro Montarroso, Andrés
Olivas Varela, José Ángel
Serrano-Guerrero, Jesús
author_role author
author2 Menéndez , Víctor Hugo
Romero Chicharro, Francisco Pascual
Montoro Montarroso, Andrés
Olivas Varela, José Ángel
Serrano-Guerrero, Jesús
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Educational data mining
Learning analytics
Multimodal feature extraction
Multimodal learning analytics
topic Educational data mining
Learning analytics
Multimodal feature extraction
Multimodal learning analytics
description Multimodal learning analytics (MMLA) has become a prominent approach for capturing the complexity of learning by integrating diverse data sources such as video, audio, physiological signals, and digital interactions. This comprehensive review synthesises findings from 177 peer-reviewed studies to examine the foundations, methodologies, tools, and applications of MMLA in education. It provides a detailed analysis of data collection modalities, feature extraction pipelines, modelling techniques—including machine learning, deep learning, and fusion strategies—and software frameworks used across various educational settings. Applications are categorised by pedagogical goals, including engagement monitoring, collaborative learning, simulation-based environments, and inclusive education. The review identifies key challenges, such as data synchronisation, model interpretability, ethical concerns, and scalability barriers. It concludes by outlining future research directions, with emphasis on real-world deployment, longitudinal studies, explainable artificial intelligence, emerging modalities, and cross-cultural validation. This work aims to consolidate current knowledge, address gaps in practice, and offer practical guidance for researchers and practitioners advancing multimodal approaches in education.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://doi.org/10.3390/app15115896
https://hdl.handle.net/10578/43686
url https://doi.org/10.3390/app15115896
https://hdl.handle.net/10578/43686
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2019-104735RB-C42 (ERA/ERDF, EU)
PLEC2021-007681
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv Applied sciences, 2025, 15, 5896
reponame:RUIdeRA. Repositorio Institucional de la UCLM
instname:Universidad de Castilla-La Mancha
instname_str Universidad de Castilla-La Mancha
reponame_str RUIdeRA. Repositorio Institucional de la UCLM
collection RUIdeRA. Repositorio Institucional de la UCLM
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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