Abstract—There is increasing evidence that learners’
affective and cognitive states play a key role in the learning
process. This suggests that systems which are able to detect
these states can dynamically use adapted strategies to increase
the pace of the learners’ skill acquisition and improve their
learning experience. In this work, we present a novel approach
for automatically adapting the learning strategy in real-time
according to the learner’s detected mental state. The main goal
of the approach is to maintain the learner in a positive state
during a lesson by adaptively selecting the best interaction
strategy between either using problem solving or worked
examples. Two mental indexes, namely, cognitive load and
mental engagement were extracted from electroencephalogram
(EEG) signals, and used to adapt the system’s interaction. The
cognitive load index was developped by training and validating
a prediction model on various types of memory and logical tasks.
The engagement index was directly computed from the EEG
signal frequency bands. An experiment with 14 learners was
performed in order to evaluate this approach. The obtained
results showed that using the learner’s mental state to adapt the
system’s interaction has a positive impact on the learning
outcomes, the learning experience and the learners’ reported
Index Terms—Adaptive system, mental engagement, cognitive load, EEG, affect, learning performance, learning experience.
M. Chaouachi, I. Jraidi, and S. P. Lajoie are with the Department of Educational and Counselling Psychology, McGill University, 3700 McTavish Street, Montréal, QC H3A 1Y2, Canada (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
C. Frasson is with the Department of Computer Science and Operations Research, University of Montreal, 2920 Chemin de la Tour, Montréal, H3T-1J8 QC, Canada (e-mail: email@example.com).
Cite: Maher Chaouachi, Imène Jraidi, Susanne P. Lajoie, and Claude Frasson, "Enhancing the Learning Experience Using Real-Time Cognitive Evaluation," International Journal of Information and Education Technology vol. 9, no. 10, pp. 678-688, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).