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

doi:10.1145/3027385.3027431

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 proceeding › Conference contribution

65 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 language	English (US)
Title of host publication	LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference
Subtitle of host publication	Understanding, Informing and Improving Learning with Data
Publisher	Association for Computing Machinery
Pages	80-89
Number of pages	10
ISBN (Electronic)	9781450348706
DOIs	https://doi.org/10.1145/3027385.3027431
State	Published - Mar 13 2017
Externally published	Yes
Event	7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada Duration: Mar 13 2017 → Mar 17 2017

Publication series

Name	ACM International Conference Proceeding Series

Other

Other	7th International Conference on Learning Analytics and Knowledge, LAK 2017
Country/Territory	Canada
City	Vancouver
Period	3/13/17 → 3/17/17

Bibliographical note

Publisher Copyright:
© 2017 ACM.

Keywords

Cognitive load
EEG
Engagement
Intelligent tutoring systems

Publisher link

10.1145/3027385.3027431

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Mills, C., Fridman, I., Soussou, W., Waghray, D., Olney, A. M., & D'Mello, S. K. (2017). Put your thinking cap on: Detecting cognitive load using EEG during learning. In LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data (pp. 80-89). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3027385.3027431

Put your thinking cap on: Detecting cognitive load using EEG during learning. / Mills, Caitlin; Fridman, Igor; Soussou, Walid et al.
LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. p. 80-89 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mills, C, Fridman, I, Soussou, W, Waghray, D, Olney, AM & D'Mello, SK 2017, Put your thinking cap on: Detecting cognitive load using EEG during learning. in LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 80-89, 7th International Conference on Learning Analytics and Knowledge, LAK 2017, Vancouver, Canada, 3/13/17. https://doi.org/10.1145/3027385.3027431

Mills C, Fridman I, Soussou W, Waghray D, Olney AM, D'Mello SK. Put your thinking cap on: Detecting cognitive load using EEG during learning. 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. 80-89. (ACM International Conference Proceeding Series). doi: 10.1145/3027385.3027431

Mills, Caitlin ; Fridman, Igor ; Soussou, Walid et al. / Put your thinking cap on : Detecting cognitive load using EEG during learning. LAK 2017 Conference Proceedings - 7th International Learning Analytics and Knowledge Conference: Understanding, Informing and Improving Learning with Data. Association for Computing Machinery, 2017. pp. 80-89 (ACM International Conference Proceeding Series).

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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.",

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AB - 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.

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

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