{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T11:06:32Z","timestamp":1772449592482,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T00:00:00Z","timestamp":1771891200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000780","name":"European Union","doi-asserted-by":"publisher","award":["ESPA 2021\u20132027"],"award-info":[{"award-number":["ESPA 2021\u20132027"]}],"id":[{"id":"10.13039\/501100000780","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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).<\/jats:p>","DOI":"10.3390\/fi18030115","type":"journal-article","created":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T16:58:18Z","timestamp":1771952298000},"page":"115","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Survey of Multimodal Learning Analytics: Data, Methods, Systems, and Responsible Deployment"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7374-0099","authenticated-orcid":false,"given":"Georgios","family":"Kostopoulos","sequence":"first","affiliation":[{"name":"School of Social Sciences, Hellenic Open University, 26331 Patras, Greece"},{"name":"Department of Mathematics, University of Patras, 26504 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2247-3082","authenticated-orcid":false,"given":"Sotiris","family":"Kotsiantis","sequence":"additional","affiliation":[{"name":"School of Social Sciences, Hellenic Open University, 26331 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4044-6018","authenticated-orcid":false,"given":"Theodor","family":"Panagiotakopoulos","sequence":"additional","affiliation":[{"name":"Department of Management Science and Technology, University of Patras, 26334 Patras, Greece"},{"name":"School of Business, University of Nicosia, 1700 Nicosia, Cyprus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1004-7860","authenticated-orcid":false,"given":"Achilles","family":"Kameas","sequence":"additional","affiliation":[{"name":"School of Technology and Science, Hellenic Open University, 26335 Patras, Greece"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Becerra, A., Cobos, R., and Lang, C. 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