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

Publikation: Bidrag til bog/antologi/rapportKonferenceartikel i proceedingpeer review

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, 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.
OriginalsprogEngelsk
TitelLAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference : Understanding, Informing and Improving Learning with Data
Antal sider2
ForlagAssociation for Computing Machinery
Publikationsdato14 mar. 2017
Sider518-519
ISBN (Elektronisk)978-1-4503-4870-6
DOI
StatusUdgivet - 14 mar. 2017
BegivenhedSixth Multimodal Learning Analytics Workshop - Simon Fraser University, Vancouver, Canada
Varighed: 14 mar. 201714 dec. 2017
http://educ-lak17.educ.sfu.ca/index.php/pre-conference-events/

Workshop

WorkshopSixth Multimodal Learning Analytics Workshop
LokationSimon Fraser University
Land/OmrådeCanada
ByVancouver
Periode14/03/1714/12/17
Internetadresse

Emneord

  • Læring, pædagogik og undervisning
  • Multimodal Learning Analytics

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