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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/6238

    Título
    Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
    Autor
    Sáiz Manzanares, María ConsueloUBU authority
    Ramos Pérez, Ismael
    Arnaiz Rodríguez, Adrián
    Rodríguez Arribas, Sandra
    Almeida, Leandro
    Martin, Caroline Françoise
    Publicado en
    Applied Sciences. 2021, V. 11, n. 13, 6157
    Editorial
    MDPI
    Fecha de publicación
    2021-07
    ISSN
    2076-3417
    DOI
    10.3390/app11136157
    Abstract
    In recent decades, the use of technological resources such as the eye tracking methodology is providing cognitive researchers with important tools to better understand the learning process. However, the interpretation of the metrics requires the use of supervised and unsupervised learning techniques. The main goal of this study was to analyse the results obtained with the eye tracking methodology by applying statistical tests and supervised and unsupervised machine learning techniques, and to contrast the effectiveness of each one. The parameters of fixations, saccades, blinks and scan path, and the results in a puzzle task were found. The statistical study concluded that no significant differences were found between participants in solving the crossword puzzle task; significant differences were only detected in the parameters saccade amplitude minimum and saccade velocity minimum. On the other hand, this study, with supervised machine learning techniques, provided possible features for analysis, some of them different from those used in the statistical study. Regarding the clustering techniques, a good fit was found between the algorithms used (k-means ++, fuzzy k-means and DBSCAN). These algorithms provided the learning profile of the participants in three types (students over 50 years old; and students and teachers under 50 years of age). Therefore, the use of both types of data analysis is considered complementary.
    Palabras clave
    Machine learning
    Cognition
    Eye tracking
    Instance selection
    Clustering
    Information processing
    Materia
    Enseñanza
    Teaching
    Psicología
    Psychology
    Tecnología
    Technology
    URI
    http://hdl.handle.net/10259/6238
    Versión del editor
    https://doi.org/10.3390/app11136157
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