Authors
Caitlin Mills, Igor Fridman, Walid Soussou, Disha Waghray, Andrew M Olney, Sidney K D'Mello
Publication date
2017/3/13
Book
Proceedings of the seventh international learning analytics & knowledge conference
Pages
80-89
Description
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.
Total citations
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Scholar articles
C Mills, I Fridman, W Soussou, D Waghray, AM Olney… - Proceedings of the seventh international learning …, 2017