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.

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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.

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