Predicting Stress Level Variation from Learner Characteristics and Brainwaves

Authors:
Alicia Heraz;Imè/ne Jraidi;Maher Chaouachi;Claude Frasson
Affiliations:
HERON Lab/ Computer Science Department/ University of Montré/al, CP 6128 succ. Centre Ville Montré/al, QC, H3T-1J4, Canada, {herazali, jraidiim, chaouacm, frasson}@iro.umontreal.ca;HERON Lab/ Computer Science Department/ University of Montré/al, CP 6128 succ. Centre Ville Montré/al, QC, H3T-1J4, Canada, {herazali, jraidiim, chaouacm, frasson}@iro.umontreal.ca;HERON Lab/ Computer Science Department/ University of Montré/al, CP 6128 succ. Centre Ville Montré/al, QC, H3T-1J4, Canada, {herazali, jraidiim, chaouacm, frasson}@iro.umontreal.ca;HERON Lab/ Computer Science Department/ University of Montré/al, CP 6128 succ. Centre Ville Montré/al, QC, H3T-1J4, Canada, {herazali, jraidiim, chaouacm, frasson}@iro.umontreal.ca
Venue:
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Year:
2009

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Abstract

It is very common that students fail their exam because of an excessive stress. However, an attitude with no stress at all can cause the same thing as stress can be beneficial in some learning cases. Studying the stress level variation can then be very useful in a learning environment. In this paper, the aim is predicting the stress level variation of the learner in relation to his electrical brain activity within an experiment over two days. To attain that goal, three personal and non-personal characteristics were used: gender, usual mode of study and dominant activity between the first and second day. 21 participants were recruited for our experiment. Results were very encouraging: an accuracy of 71% was obtained by using the ID3 machine learning algorithm.