Predicting Student’s Attributes from their Physiological Response to an Online Course

Authors

  • Marco Antonio Hernández Pérez Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Emmanuel Rosado Martínez Instituto Politécnico Nacional, Escuela Superior de Cómputo
  • Rolando Menchaca Méndez Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Ricardo Menchaca Méndez Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Mario E. Rivero Ángeles Instituto Politécnico Nacional, Centro de Investigación en Computación
  • Víctor M. González Instituto Tecnológico Autónomo de México

DOI:

https://doi.org/10.13053/cys-23-4-3050

Keywords:

Machine learning, electroencephalography, physiological response, e-learning

Abstract

In this work, we present the results of a study where we monitored the physiological response of a set of fifty high-school students during their participation in an online course. For each of the subjects, we recollected time-series obtained from sensors of 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 Vector Machine models that showed to be effective for determining the sex of the subjects, as well as the type of activity they were performing, her learning style and whether or not they had previous knowledge about the course contents. These results show that the physiological signals contain relevant information about the characteristics of a user of an online learning platform and that this information can be extracted to develop better online learning tools.

Author Biography

Marco Antonio Hernández Pérez, Instituto Politécnico Nacional, Centro de Investigación en Computación

Laboratorio de Redes y Ciencia de Datos - CIC IPN

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Published

2019-12-20

Issue

Vol. 23 No. 4 (2019): 23-4(2019)

Section

Articles

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