Current and future multimodal learning analytics data challenges

Daniel Spikol, Marcelo Worsley, Luis P. Prieto, Xavier Ochoa, M. J. Rodríguez-Triana, Mutlu Cukurova

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publicationUnderstanding, Informing and Improving Learning with Data
PublisherAssociation for Computing Machinery
Pages518-519
Number of pages2
ISBN (Electronic)9781450348706
DOIs
StatePublished - Mar 13 2017
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Other

Other7th International Conference on Learning Analytics and Knowledge, LAK 2017
CountryCanada
CityVancouver
Period3/13/173/17/17

Fingerprint

Artificial intelligence
Learning systems
Availability
Sensors

Keywords

  • Challenges
  • Datasets
  • Multimodal learning analytics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Spikol, D., Worsley, M., Prieto, L. P., Ochoa, X., Rodríguez-Triana, M. J., & Cukurova, M. (2017). Current and future multimodal learning analytics data challenges. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 518-519). Association for Computing Machinery. https://doi.org/10.1145/3027385.3029437

Current and future multimodal learning analytics data challenges. / Spikol, Daniel; Worsley, Marcelo; Prieto, Luis P.; Ochoa, Xavier; Rodríguez-Triana, M. J.; Cukurova, Mutlu.

LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. p. 518-519.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Spikol, D, Worsley, M, Prieto, LP, Ochoa, X, Rodríguez-Triana, MJ & Cukurova, M 2017, Current and future multimodal learning analytics data challenges. in LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, pp. 518-519, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 3/13/17. https://doi.org/10.1145/3027385.3029437
Spikol D, Worsley M, Prieto LP, Ochoa X, Rodríguez-Triana MJ, Cukurova M. Current and future multimodal learning analytics data challenges. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery. 2017. p. 518-519 https://doi.org/10.1145/3027385.3029437
Spikol, Daniel ; Worsley, Marcelo ; Prieto, Luis P. ; Ochoa, Xavier ; Rodríguez-Triana, M. J. ; Cukurova, Mutlu. / Current and future multimodal learning analytics data challenges. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. pp. 518-519
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