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|
|Number of pages||10|
|State||Published - Mar 13 2017|
|Event||7th International Conference on Learning Analytics and Knowledge, LAK 2017 - Vancouver, Canada|
Duration: Mar 13 2017 → Mar 17 2017
|Name||ACM International Conference Proceeding Series|
|Other||7th International Conference on Learning Analytics and Knowledge, LAK 2017|
|Period||3/13/17 → 3/17/17|
Bibliographical noteFunding 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.
© 2017 ACM.
- Cognitive load
- Intelligent tutoring systems