2024-03-28T23:25:33Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/62472023-08-28T12:06:49Zcom_10259.4_2503com_10259_5086com_10259_2604com_10259_4219com_10259_5841col_10259.4_2504col_10259_4220col_10259_5842
Sáiz Manzanares, María Consuelo
510
600
0000-0002-1736-2089
Rodríguez Diez, Juan José
477
600
0000-0002-3291-2739
Marticorena Sánchez, Raúl
338
600
0000-0002-2633-635X
Zaparaín Yáñez, Mª José
581
600
0000-0002-1443-4964
Cerezo Menéndez, Rebeca
642e624f-27a4-40ea-b6bf-b6817342ce65
600
2021-11-29T08:57:40Z
2021-11-29T08:57:40Z
2020-03
2071-1050
http://hdl.handle.net/10259/6247
10.3390/su12051970
The use of learning environments that apply Advanced Learning Technologies (ALTs) and Self-Regulated Learning (SRL) is increasingly frequent. In this study, eye-tracking technology was used to analyze scan-path differences in a History of Art learning task. The study involved 36 participants (students versus university teachers with and without previous knowledge). The scan-paths were registered during the viewing of video based on SRL. Subsequently, the participants were asked to solve a crossword puzzle, and relevant vs. non-relevant Areas of Interest (AOI) were defined. Conventional statistical techniques (ANCOVA) and data mining techniques (string-edit methods and k-means clustering) were applied. The former only detected differences for the crossword puzzle. However, the latter, with the Uniform Distance model, detected the participants with the most effective scan-path. The use of this technique successfully predicted 64.9% of the variance in learning results. The contribution of this study is to analyze the teaching–learning process with resources that allow a personalized response to each learner, understanding education as a right throughout life from a sustainable perspective.
European Project “Self-Regulated Learning in SmartArt” 2019-1-ES01-KA204-065615 and the Research Funding Program (Funding of dissemination of research results, 2020) of the Vice-Rectorate for Research and Knowledge Transfer of the University of Burgos to the Recognized Investigation Group DATAHES.
application/pdf
eng
MDPI
Sustainability. 2020, V. 12, n. 5, 1970
https://doi.org/10.3390/su12051970
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
Advanced learning technologies
Lifelong learning
Sustainability education
Eye tracking
Data mining techniques
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
THUMBNAIL
Saiz-sustainability_2020.pdf.jpg
Saiz-sustainability_2020.pdf.jpg
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LICENSE
license.txt
license.txt
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https://riubu.ubu.es/bitstream/10259/6247/3/license.txt
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https://riubu.ubu.es/bitstream/10259/6247/2/license_rdf
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ORIGINAL
Saiz-sustainability_2020.pdf
Saiz-sustainability_2020.pdf
application/pdf
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https://riubu.ubu.es/bitstream/10259/6247/1/Saiz-sustainability_2020.pdf
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10259/6247
oai:riubu.ubu.es:10259/6247
2023-08-28 14:06:49.214
Repositorio Institucional de la Universidad de Burgos
bubrep@ubu.es
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