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Research trends in multimodal learning analytics: A systematic mapping study
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-9278-8063
Copenhagen University, Copenhagen, Denmark.
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).
2023 (engelsk)Inngår i: Computers and Education: Artificial Intelligence, ISSN 2666-920X, Vol. 4, s. 100136-100136, artikkel-id 100136Artikkel, forskningsoversikt (Fagfellevurdert) Published
Abstract [en]

Understanding and improving education are critical goals of learning analytics. However, learning is not always mediated or aided by a digital system that can capture digital traces. Learning in such environments can be studied by recording, processing, and analyzing different signals, including video and audio, so that traces of actors’ actions and interactions are captured. Multimodal Learning Analytics refers to analyzing these signals through the use and integration of these multiple modes. However, a need exists to evaluate how research is conducted in the emerging field of multimodal learning analytics to aid and evaluate how these systems work. With the growth of multimodal learning analytics, research trends and technologies are needed to support its development. We conducted a systematic mapping study based on established systematic literature practices to identify multimodal learning analytics research types, methodologies, and trending research themes. Most mapped papers presented different solutions and used evaluation-based research methods to demonstrate an increasing interest in multimodal learning analytics technologies. In addition, we identified 14 topics under four themes––learning context, learning process, systems and modality, and technologies––that can contribute to the growth of multimodal learning analytics.

sted, utgiver, år, opplag, sider
Elsevier, 2023. Vol. 4, s. 100136-100136, artikkel-id 100136
Emneord [en]
Multimodal learning analytics, Mapping study, Learning technologies, Artificial intelligence
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Identifikatorer
URN: urn:nbn:se:mau:diva-64288DOI: 10.1016/j.caeai.2023.100136Scopus ID: 2-s2.0-85151456109OAI: oai:DiVA.org:mau-64288DiVA, id: diva2:1818876
Tilgjengelig fra: 2023-12-12 Laget: 2023-12-12 Sist oppdatert: 2023-12-12bibliografisk kontrollert

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Ouhaichi, HamzaVogel, Bahtijar

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