Put your thinking cap on: Detecting cognitive load using EEG during learning

Caitlin Mills, Igor Fridman, Walid Soussou, Disha Waghray, Andrew M. Olney, Sidney K. D'Mello

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

51 Scopus citations

Abstract

Current learning technologies have no direct way to assess students' mental effort: are they in deep thought, struggling to overcome an impasse, or are they zoned out? To address this challenge, we propose the use of EEG-based cognitive load detectors during learning. Despite its potential, EEG has not yet been utilized as a way to optimize instructional strategies. We take an initial step towards this goal by assessing how experimentally manipulated (easy and difficult) sections of an intelligent tutoring system (ITS) influenced EEG-based estimates of students' cognitive load. We found a main effect of task difficulty on EEG-based cognitive load estimates, which were also correlated with learning performance. Our results show that EEG can be a viable source of data to model learners' mental states across a 90-minute session.

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
Pages80-89
Number of pages10
ISBN (Electronic)9781450348706
DOIs
StatePublished - Mar 13 2017
Externally publishedYes
Event7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada
Duration: Mar 13 2017Mar 17 2017

Publication series

NameACM International Conference Proceeding Series

Other

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

Bibliographical note

Funding Information:
This research was supported by the National Science Foundation (NSF) (IIP 1416595; DRL 1108845; IIS 1523091). Any opinions, findings and conclusions, or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.

Publisher Copyright:
© 2017 ACM.

Keywords

  • Cognitive load
  • EEG
  • Engagement
  • Intelligent tutoring systems

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