EEG and Physiological Signals Dataset from Participants during Traditional and Partially Immersive Learning Experiences in Humanities

Rebeca Romo- De León, Mei Li L. Cham-Pérez, Verónica Andrea Elizondo-Villegas, Alejandro Villarreal-Villarreal, Alexandro Antonio Ortiz-Espinoza, Carol Stefany Vélez-Saboyá, Jorge de Jesús Lozoya-Santos, Manuel Cebral-Loureda and Mauricio A. Ramírez-Moreno ()
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Rebeca Romo- De León: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Mei Li L. Cham-Pérez: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Verónica Andrea Elizondo-Villegas: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Alejandro Villarreal-Villarreal: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Alexandro Antonio Ortiz-Espinoza: School of Humanities and Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
Carol Stefany Vélez-Saboyá: School of Humanities and Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
Jorge de Jesús Lozoya-Santos: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico
Manuel Cebral-Loureda: School of Humanities and Education, Tecnologico de Monterrey, Monterrey 64849, Mexico
Mauricio A. Ramírez-Moreno: Mechatronics Department, School of Engineering and Sciences, Tecnologico de Monterrey, Monterrey 64849, Mexico

Data, 2024, vol. 9, issue 5, 1-15

Abstract: The relevance of the interaction between Humanities-enhanced learning using immersive environments and simultaneous physiological signal analysis contributes to the development of Neurohumanities and advancements in applications of Digital Humanities. The present dataset consists of recordings from 24 participants divided in two groups (12 participants in each group) engaging in simulated learning scenarios, traditional learning, and partially immersive learning experiences. Data recordings from each participant contain recordings of physiological signals and psychometric data collected from applied questionnaires. Physiological signals include electroencephalography, real-time engagement and emotion recognition calculation by a Python EEG acquisition code, head acceleration, electrodermal activity, blood volume pressure, inter-beat interval, and temperature. Before the acquisition of physiological signals, participants were asked to fill out the General Health Questionnaire and Trait Meta-Mood Scale. In between recording sessions, participants were asked to fill out Likert-scale questionnaires regarding their experience and a Self-Assessment Manikin. At the end of the recording session, participants filled out the ITC Sense of Presence Inventory questionnaire for user experience. The dataset can be used to explore differences in physiological patterns observed between different learning modalities in the Humanities.

Keywords: neuroeducation; neurohumanities; immersive spaces; neuroengineering; educational innovation (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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