New Architecture of a Multi Agent System which Measures the Learner Brainwaves to Predict his Stress Level Variation
PROCEEDINGS
Imène Jraidi, Alicia Heraz, Maher Chaouachi, Claude Frasson, Université de Montréal, Canada
E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in Vancouver, Canada ISBN 978-1-880094-76-1 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA
Abstract
The stress factor plays an important role in learning tasks, especially when the learner is in front of an exam. Studying the stress level variation can then be very useful in a learning situation. In this paper, we conducted an experiment with 21 participants over a two day period. We have two objectives; the first one is to predict the stress level variation of the learner in relation to his electrical brain activity. To attain that goal, three personal and non-personal characteristics were used: the gender, the usual mode of study and the dominant activity between the first and second day of the experiment. An accuracy of 71% was obtained by using the ID3 machine learning algorithm. The second goal is to propose an extension of a Multi Agent System (MAS) by adding the Stress Prediction Agent. This MAS uses Brainwaves to predict certain learner characteristics.
Citation
Jraidi, I., Heraz, A., Chaouachi, M. & Frasson, C. (2009). New Architecture of a Multi Agent System which Measures the Learner Brainwaves to Predict his Stress Level Variation. In T. Bastiaens, J. Dron & C. Xin (Eds.), Proceedings of E-Learn 2009--World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 2726-2733). Vancouver, Canada: Association for the Advancement of Computing in Education (AACE). Retrieved March 19, 2024 from https://www.learntechlib.org/primary/p/32871/.
© 2009 Association for the Advancement of Computing in Education (AACE)