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Current and future multimodal learning analytics data challenges

Spikol, D; Worsley, M; Prieto, LP; Ochoa, X; Rodríguez-Triana, MJ; Cukurova, M; (2017) Current and future multimodal learning analytics data challenges. In: Wise, A and Winne, PH and Lynch, G and Ochoa, X and Molenaar, I and Dawson, S and Hatala, M, (eds.) LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference. (pp. pp. 518-519). Association for Computing Machinery (ACM): New York, NY, USA. Green open access

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Abstract

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, highfrequency 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.

Type: Proceedings paper
Title: Current and future multimodal learning analytics data challenges
Event: Seventh International Learning Analytics & Knowledge Conference (LAK '17)
ISBN-13: 9781450348706
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3027385.3029437
Publisher version: http://dx.doi.org/10.1145/3027385.3029437
Language: English
Additional information: Multimodal learning analytics; datasets; challenges
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education
UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Culture, Communication and Media
URI: https://discovery.ucl.ac.uk/id/eprint/1547682
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