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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/10273

    Título
    A Proposed Method of Automating Data Processing for Analysing Data Produced from Eye Tracking and Galvanic Skin Response
    Autor
    Sáez García, Javier
    Sáiz Manzanares, María ConsueloUBU authority Orcid
    Marticorena Sánchez, RaúlUBU authority Orcid
    Publicado en
    Computers. 2024, V. 13, n. 11, 289
    Editorial
    MDPI
    Fecha de publicación
    2024
    DOI
    10.3390/computers13110289
    Abstract
    The use of eye tracking technology, together with other physiological measurements such as psychogalvanic skin response (GSR) and electroencephalographic (EEG) recordings, provides researchers with information about users’ physiological behavioural responses during their learning process in different types of tasks. These devices produce a large volume of data. However, in order to analyse these records, researchers have to process and analyse them using complex statistical and/or machine learning techniques (supervised or unsupervised) that are usually not incorporated into the devices. The objectives of this study were (1) to propose a procedure for processing the extracted data; (2) to address the potential technical challenges and difficulties in processing logs in integrated multichannel technology; and (3) to offer solutions for automating data processing and analysis. A Notebook in Jupyter is proposed with the steps for importing and processing data, as well as for using supervised and unsupervised machine learning algorithms.
    Palabras clave
    Eye tracking
    Galvanic skin response
    Behavioural monitoring
    Learning process
    Data processing
    Materia
    Enseñanza superior
    Education, Higher
    Tecnología
    Technology
    Psicología
    Psychology
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/10273
    Versión del editor
    https://doi.org/10.3390/computers13110289
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    Documento(s) sujeto(s) a una licencia Creative Commons Atribución 4.0 Internacional
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    Sáez-computers_2024.pdf
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    6.586Mb
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