A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment

Kostopoulos, Georgios; Kotsiantis, Sotiris; Panagiotakopoulos, Theodor; Kameas, Achilles

doi:10.3390/fi18030115

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A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment

by

Georgios Kostopoulos

^1,2

,

Sotiris Kotsiantis

^1,*

,

Theodor Panagiotakopoulos

^3,4

and

Achilles Kameas

⁵

¹

School of Social Sciences, Hellenic Open University, 26331 Patras, Greece

²

Department of Mathematics, University of Patras, 26504 Patras, Greece

³

Department of Management Science and Technology, University of Patras, 26334 Patras, Greece

⁴

School of Business, University of Nicosia, 1700 Nicosia, Cyprus

⁵

School of Technology and Science, Hellenic Open University, 26335 Patras, Greece

^*

Author to whom correspondence should be addressed.

Future Internet 2026, 18(3), 115; https://doi.org/10.3390/fi18030115

Submission received: 1 February 2026 / Revised: 16 February 2026 / Accepted: 23 February 2026 / Published: 24 February 2026

(This article belongs to the Special Issue Generative Artificial Intelligence: Systems, Technologies and Applications)

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Abstract

Multimodal Learning Analytics (MMLA) is an extension of Learning Analytics that combines multiple data streams such as audio, video, physiological signals, logs, and spatial trails to analyze learning processes that cannot be easily captured through any single modality. This review synthesizes research on sensing and instrumentation, feature extraction, multimodal fusion, modeling approaches, and end-to-end systems that provide feedback and support reflection. We also discuss how generative AI and Large Language Models (LLMs) increasingly improve MMLA pipelines by enabling scalable semantic and pragmatic analysis of learner discourse and interaction. In addition, we review robustness issues that arise when working with real-world data (e.g., noise, missing data, and scalability) and responsible deployment issues such as privacy and student-focused views of fairness, accountability, transparency, and ethics (FATE).

Keywords: Multimodal Learning Analytics; collaborative learning; sensors; multimodal data streams; data fusion; Generative Artificial Intelligence; Large Language Models; feedback; ethics; privacy; FATE

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MDPI and ACS Style

Kostopoulos, G.; Kotsiantis, S.; Panagiotakopoulos, T.; Kameas, A. A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment. Future Internet 2026, 18, 115. https://doi.org/10.3390/fi18030115

AMA Style

Kostopoulos G, Kotsiantis S, Panagiotakopoulos T, Kameas A. A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment. Future Internet. 2026; 18(3):115. https://doi.org/10.3390/fi18030115

Chicago/Turabian Style

Kostopoulos, Georgios, Sotiris Kotsiantis, Theodor Panagiotakopoulos, and Achilles Kameas. 2026. "A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment" Future Internet 18, no. 3: 115. https://doi.org/10.3390/fi18030115

APA Style

Kostopoulos, G., Kotsiantis, S., Panagiotakopoulos, T., & Kameas, A. (2026). A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment. Future Internet, 18(3), 115. https://doi.org/10.3390/fi18030115

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment

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