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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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/13/6157 |
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| author | María Consuelo Sáiz-Manzanares Ismael Ramos Pérez Adrián Arnaiz Rodríguez Sandra Rodríguez Arribas Leandro Almeida Caroline Françoise Martin |
| author_facet | María Consuelo Sáiz-Manzanares Ismael Ramos Pérez Adrián Arnaiz Rodríguez Sandra Rodríguez Arribas Leandro Almeida Caroline Françoise Martin |
| author_sort | María Consuelo Sáiz-Manzanares |
| collection | DOAJ |
| 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 (<i>k-</i>means ++, fuzzy <i>k-</i>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. |
| first_indexed | 2024-03-10T09:52:41Z |
| format | Article |
| id | doaj.art-4313f0ff580745deb140f1906f56c723 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T09:52:41Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-4313f0ff580745deb140f1906f56c7232023-11-22T02:34:29ZengMDPI AGApplied Sciences2076-34172021-07-011113615710.3390/app11136157Analysis of the Learning Process through Eye Tracking Technology and Feature Selection TechniquesMaría Consuelo Sáiz-Manzanares0Ismael Ramos Pérez1Adrián Arnaiz Rodríguez2Sandra Rodríguez Arribas3Leandro Almeida4Caroline Françoise Martin5Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Research Group DATAHES, Pº Comendadores s/n, 09001 Burgos, SpainDepartamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avda. de Cantabria s/n, 09006 Burgos, SpainDepartamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avda. de Cantabria s/n, 09006 Burgos, SpainDepartamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group DATAHES, Escuela Politécnica Superior, Avda. de Cantabria s/n, 09006 Burgos, SpainInstituto de Educação, Universidade do Minho, Research Group CIEd, Campus de Gualtar, 4710-057 Braga, PortugalDepartamento de Filología Inglesa, Universidad de Burgos, Pº Comendadores s/n, 09001 Burgos, SpainIn 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 (<i>k-</i>means ++, fuzzy <i>k-</i>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.https://www.mdpi.com/2076-3417/11/13/6157machine learningcognitioneye trackinginstance selectionclusteringinformation processing |
| spellingShingle | María Consuelo Sáiz-Manzanares Ismael Ramos Pérez Adrián Arnaiz Rodríguez Sandra Rodríguez Arribas Leandro Almeida Caroline Françoise Martin Analysis of the Learning Process through Eye Tracking Technology and Feature Selection Techniques Applied Sciences machine learning cognition eye tracking instance selection clustering information processing |
| title | 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_short | 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 |
| topic | machine learning cognition eye tracking instance selection clustering information processing |
| url | https://www.mdpi.com/2076-3417/11/13/6157 |
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