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Current and Future ...
Current and Future Multimodal Learning Analytics Data Challenges
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- Spikol, Daniel, 1965- (author)
- Malmö högskola,Fakulteten för teknik och samhälle (TS)
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- Prieto, Luis P. (author)
- Tallinn University, Tallinn, Estonia
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- Rodriguez-Triana, M. J. (author)
- REACT Group, EPFL, Lausanne, Switzerland
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- Worsley, Marcelo (author)
- Northwestern University, Evanston, IL, United States
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- Ochoa, Xavier (author)
- ESPOL, Guayaquil, Ecuador
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- Cukurova, Mutlu (author)
- UCL Knowledge Lab, London, United Kingdom
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- Vogel, Bahtijar (author)
- Malmö högskola,Fakulteten för teknik och samhälle (TS)
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- Ruffaldi, Emanuele (author)
- Scuola Superiore Sant'Anna, Italy
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- Ringtved, Ulla Lunde (author)
- University College Nordjylland
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(creator_code:org_t)
- 2017-03-13
- 2017
- English.
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In: Seventh International Learning Analytics & Knowledge Conference (LAK'17). - New York, NY, USA : ACM Digital Library. ; , s. 518-519
- Related links:
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https://discovery.uc...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
Close
- 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.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Multimodal learning analytics
- datasets
- challenges
Publication and Content Type
- ref (subject category)
- kon (subject category)
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