Abstract

HERNANDEZ PEREZ, Marco A. et al. Predicting Student's Attributes from their Physiological Response to an Online Course. Comp. y Sist. [online]. 2019, vol.23, n.4, pp.1199-1214.  Epub Aug 09, 2021. ISSN 2007-9737.  https://doi.org/10.13053/cys-23-4-3050.

In this work, we present the results of a stud} where we monitored the physiological response of a se of fifty high-school students during their participation in an online course. For each of the subjects, we recollected time-series obtained from sensors o physiological signals such as electrical cerebral activity heart rate, galvanic skin response, body temperature among others. From the first four moments (mean variance, skewness and kurtosis) of the time-series we trained Artificial Neural Network and Support Vecto Machine models that showed to be effective fo determining the gender of the subjects, as well as the type of activity they were performing, their learning style and whether they had previous knowledge about the course contents. These results show that the physiological signals contain relevant information abou the characteristics of a user of an online learning platform and that this information can be extracted to develop better online learning tools.

Keywords : Machine learning; electroencephalography; physiological response; e-learning.

        · abstract in Spanish     · text in Spanish     · Spanish ( pdf )