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  • 1.
    Spikol, Daniel
    et al.
    Malmö högskola, Faculty of Technology and Society (TS).
    Prieto, Luis P.
    Tallinn University, Tallinn, Estonia.
    Rodriguez-Triana, M. J.
    REACT Group, EPFL, Lausanne, Switzerland.
    Worsley, Marcelo
    Northwestern University, Evanston, IL, United States.
    Ochoa, Xavier
    ESPOL, Guayaquil, Ecuador.
    Cukurova, Mutlu
    UCL Knowledge Lab, London, United Kingdom.
    Vogel, Bahtijar
    Malmö högskola, Faculty of Technology and Society (TS).
    Ruffaldi, Emanuele
    Scuola Superiore Sant'Anna, Italy.
    Ringtved, Ulla Lunde
    University College Nordjylland.
    Current and Future Multimodal Learning Analytics Data Challenges2017In: Seventh International Learning Analytics & Knowledge Conference (LAK'17), ACM Digital Library, 2017, p. 518-519Conference 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.

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