Current and future multimodal learning analytics data challenges

  • Daniel Spikol
  • Luis P. Prieto
  • M.J. Rodriguez-Triana
  • Marcelo Worsley
  • Xavier Ochoa
  • Mutlu Cukurova
  • Bahtjar Vogel
  • Emanuele Ruffaldi
  • Ulla Lunde Ringtved
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.
Flere informationer

TitelLAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
Antal sider2
ForlagAssociation for Computing Machinery
Publikationsdato14 mar. 2017
ISBN (Elektronisk)978-1-4503-4870-6
Peer reviewJa
BegivenhedSixth Multimodal Learning Analytics Workshop - Simon Fraser University, Vancouver, Canada
Varighed: 14 mar. 201714 dec. 2017


  • Tallinn University
  • REACT Group, EPFL, Lausanne
  • Northwestern University
  • ESPOL, Guayaquil, Ecuador
  • UCL Knowledge Lab, London
  • Malmö Universitet
  • Scuola Superiore Santt`Anna
  • Internet of Things and People Research Center, Malmø University
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