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Applied Sciences
Volume 15
Issue 11
10.3390/app15115896
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

A Comprehensive Review of Multimodal Analysis in Education

by
Jared D. T. Guerrero-Sosa
1,
Francisco P. Romero
1,*,
Víctor H. Menéndez-Domínguez
2,
Jesus Serrano-Guerrero
1,
Andres Montoro-Montarroso
1 and
Jose A. Olivas
1
1
Department of Information Technologies and Systems, University of Castilla La Mancha, Paseo de la Universidad, 4, 13071 Ciudad Real, Spain
2
Mathematics School, Autonomous University of Yucatan, Anillo Periférico Norte, Tablaje Cat. 13615, Merida 97119, Mexico
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(11), 5896; https://doi.org/10.3390/app15115896
Submission received: 24 April 2025 / Revised: 19 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Section Computing and Artificial Intelligence)
Versions Notes

Abstract

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.
Keywords: multimodal learning analytics; educational data mining; learning analytics; multimodal feature extraction multimodal learning analytics; educational data mining; learning analytics; multimodal feature extraction

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MDPI and ACS Style

Guerrero-Sosa, J.D.T.; Romero, F.P.; Menéndez-Domínguez, V.H.; Serrano-Guerrero, J.; Montoro-Montarroso, A.; Olivas, J.A. A Comprehensive Review of Multimodal Analysis in Education. Appl. Sci. 2025, 15, 5896. https://doi.org/10.3390/app15115896

AMA Style

Guerrero-Sosa JDT, Romero FP, Menéndez-Domínguez VH, Serrano-Guerrero J, Montoro-Montarroso A, Olivas JA. A Comprehensive Review of Multimodal Analysis in Education. Applied Sciences. 2025; 15(11):5896. https://doi.org/10.3390/app15115896

Chicago/Turabian Style

Guerrero-Sosa, Jared D. T., Francisco P. Romero, Víctor H. Menéndez-Domínguez, Jesus Serrano-Guerrero, Andres Montoro-Montarroso, and Jose A. Olivas. 2025. "A Comprehensive Review of Multimodal Analysis in Education" Applied Sciences 15, no. 11: 5896. https://doi.org/10.3390/app15115896

APA Style

Guerrero-Sosa, J. D. T., Romero, F. P., Menéndez-Domínguez, V. H., Serrano-Guerrero, J., Montoro-Montarroso, A., & Olivas, J. A. (2025). A Comprehensive Review of Multimodal Analysis in Education. Applied Sciences, 15(11), 5896. https://doi.org/10.3390/app15115896

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