An Affective-computing Approach to Provide Enhanced Learning Analytics
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
javier.dorado@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
ruben.cantarero@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
ana.rubio@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
jesus.fdezbermejo@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
xavier.deltoro@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
mariajose.santofimia@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
felix.villanueva@uclm.es
Computer Architecture and Networks Group, University of Castilla-La Mancha, Ciudad Real, Spain
juancarlos.lopez@uclm.es
CONFERENCE — 12th International Conference on Computer Supported Education
PAGES — 163-170
ISSN — 978-989-758-417-6
PUBLISHER — SciTePress
YEAR — 2020
Chaparro, J.; Navarro, R.; Ruiz, A.; Ruiz, J.; García, X.; Romero, M.; Molina, F. and López, J. (2020). An Affective-computing Approach to Provide Enhanced Learning Analytics.In Proceedings of the 12th International Conference on Computer Supported Education – Volume 1: CSEDU, ISBN 978-989-758-417-6, pages 163-170. DOI: 10.5220/0009368401630170
author={Javier Dorado Chaparro. and Rubén Cantarero Navarro. and Ana Rubio Ruiz. and Jesús Fernández{-}Bermejo Ruiz. and Xavier Del Toro García. and María José Santofimia Romero. and Félix Jesús Villanueva Molina. and Juan Carlos López López.},
title={An Affective-computing Approach to Provide Enhanced Learning Analytics},
booktitle={Proceedings of the 12th International Conference on Computer Supported Education – Volume 1: CSEDU,},
year={2020},
pages={163-170},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009368401630170},
isbn={978-989-758-417-6},
}
Abstract
Detecting emotions in a learning environment can make the student-learning process more efficient, avoiding stressful situations that might eventually lead to failure, frustation and demotivation. The work presented here describes a perceptive desktop devised to capture the sensations of any person facing learning activities. To this end, we propose a perceptive environment enhanced with capabilities to perform an analysis of electroencephalography, facial expression, eye tracking and particularly a very distinctive indicator of stress as it is the galvanic response of the skin. This work focuses on the galvanic response of the skin, comparing the performance of two devices in the context of the perceptive desktop. One of the devices was very attractive to our environment as it was a mouse that fit very well to our computer-based desktop, equipped with low-cost sensors to detect the galvanic response. The other device is more tedious to place and more expensive but we use it as a refe rence to know if the mouse is accurate. Four people were exposed to an experiment with the two devices connected, and observing the results it can be concluded that there is no correlation between the captures of both devices. Therefore, we could not select the mouse for our environment even though at first it looks like a very promising device.