2026-04-17T20:10:47Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/62382022-12-02T11:23:13Zcom_10259_5841com_10259_5086com_10259_2604com_10259_6190com_10259_6189com_10259.4_106com_10259_4438com_10259_4437col_10259_5842col_10259_6191col_10259_4439

Sáiz Manzanares, María Consuelo Ramos Pérez, Ismael Arnaiz Rodríguez, Adrián Rodríguez Arribas, Sandra Almeida, Leandro Martin, Caroline Françoise 2021-11-25T13:57:49Z 2021-11-25T13:57:49Z 2021-07 2076-3417 http://hdl.handle.net/10259/6238 10.3390/app11136157 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. European Project “Self-Regulated Learning in SmartArt” 2019-1-ES01-KA204-065615. application/pdf eng MDPI Applied Sciences. 2021, V. 11, n. 13, 6157 https://doi.org/10.3390/app11136157 info:eu-repo/grantAgreement/EC/Erasmus+/2019-1-ES01-KA204-065615/EU/SELF-REGULATED LEARNING IN SMARTART Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Machine learning Cognition Eye tracking Instance selection Clustering Information processing Enseñanza Psicología Tecnología Teaching Psychology Technology Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion