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Current and Future Multimodal Learning Analytics Data Challenges
Malmö högskola, Faculty of Technology and Society (TS).ORCID iD: 0000-0001-9454-0793
Tallinn University, Tallinn, Estonia.
REACT Group, EPFL, Lausanne, Switzerland.
Northwestern University, Evanston, IL, United States.
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2017 (English)In: Seventh International Learning Analytics & Knowledge Conference (LAK'17), ACM Digital Library, 2017, p. 518-519Conference paper, Published paper (Refereed)
Abstract [en]

Multimodal Learning Analytics (MMLA) captures, integrates and analyzes learning traces from different sources in order to obtain a more holistic understanding of the learning process, wherever it happens. MMLA leverages the increasingly widespread availability of diverse sensors, high-frequency data collection technologies and sophisticated machine learning and artificial intelligence techniques. The aim of this workshop is twofold: first, to expose participants to, and develop, different multimodal datasets that reflect how MMLA can bring new insights and opportunities to investigate complex learning processes and environments; second, to collaboratively identify a set of grand challenges for further MMLA research, built upon the foundations of previous workshops on the topic.

Place, publisher, year, edition, pages
ACM Digital Library, 2017. p. 518-519
Keywords [en]
Multimodal learning analytics, datasets, challenges
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:mau:diva-39610DOI: 10.1145/3027385.3029437ISI: 000570180700077Scopus ID: 2-s2.0-85016493237OAI: oai:DiVA.org:mau-39610DiVA, id: diva2:1520593
Conference
LAK '17: Seventh International Learning Analytics & Knowledge Conference, March 2017
Available from: 2021-01-21 Created: 2021-01-21 Last updated: 2024-04-04Bibliographically approved

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Spikol, DanielVogel, Bahtijar

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Spikol, DanielOchoa, XavierCukurova, MutluVogel, Bahtijar
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