Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques

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...

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Autores: Sáiz Manzanares, María Consuelo, Ramos Pérez, Ismael, Arnaiz Rodríguez, Adrián, Rodríguez Arribas, Sandra, Almeida, Leandro, Martin, Caroline Françoise
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2021
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/6238
Acceso en línea:http://hdl.handle.net/10259/6238
Access Level:acceso abierto
Palabra clave:Machine learning
Cognition
Eye tracking
Instance selection
Clustering
Information processing
Enseñanza
Psicología
Tecnología
Teaching
Psychology
Technology
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spelling Analysis of the Learning Process through Eye Tracking Technology and Feature Selection TechniquesSáiz Manzanares, María ConsueloRamos Pérez, IsmaelArnaiz Rodríguez, AdriánRodríguez Arribas, SandraAlmeida, LeandroMartin, Caroline FrançoiseMachine learningCognitionEye trackingInstance selectionClusteringInformation processingEnseñanzaPsicologíaTecnologíaTeachingPsychologyTechnologyIn 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.MDPI202120212021info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10259/6238reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)instname:Universidad de Burgos (UBU)InglésApplied Sciences. 2021, V. 11, n. 13, 6157https://doi.org/10.3390/app11136157info:eu-repo/grantAgreement/EC/Erasmus+/2019-1-ES01-KA204-065615Atribución 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riubu.ubu.es:10259/62382026-05-28T07:56:11Z
dc.title.none.fl_str_mv Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
title Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
spellingShingle Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
Sáiz Manzanares, María Consuelo
Machine learning
Cognition
Eye tracking
Instance selection
Clustering
Information processing
Enseñanza
Psicología
Tecnología
Teaching
Psychology
Technology
title_short Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
title_full Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
title_fullStr Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
title_full_unstemmed Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
title_sort Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques
dc.creator.none.fl_str_mv Sáiz Manzanares, María Consuelo
Ramos Pérez, Ismael
Arnaiz Rodríguez, Adrián
Rodríguez Arribas, Sandra
Almeida, Leandro
Martin, Caroline Françoise
author Sáiz Manzanares, María Consuelo
author_facet Sáiz Manzanares, María Consuelo
Ramos Pérez, Ismael
Arnaiz Rodríguez, Adrián
Rodríguez Arribas, Sandra
Almeida, Leandro
Martin, Caroline Françoise
author_role author
author2 Ramos Pérez, Ismael
Arnaiz Rodríguez, Adrián
Rodríguez Arribas, Sandra
Almeida, Leandro
Martin, Caroline Françoise
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Machine learning
Cognition
Eye tracking
Instance selection
Clustering
Information processing
Enseñanza
Psicología
Tecnología
Teaching
Psychology
Technology
topic Machine learning
Cognition
Eye tracking
Instance selection
Clustering
Information processing
Enseñanza
Psicología
Tecnología
Teaching
Psychology
Technology
description 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.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10259/6238
url http://hdl.handle.net/10259/6238
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv 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
dc.rights.none.fl_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Atribución 4.0 Internacional
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Repositorio Institucional de la Universidad de Burgos (RIUBU)
instname:Universidad de Burgos (UBU)
instname_str Universidad de Burgos (UBU)
reponame_str Repositorio Institucional de la Universidad de Burgos (RIUBU)
collection Repositorio Institucional de la Universidad de Burgos (RIUBU)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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