Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques

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 (studen...

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Detalles Bibliográficos
Autores: Sáiz Manzanares, María Consuelo, Rodríguez Diez, Juan José, Marticorena Sánchez, Raúl, Zaparaín Yáñez, Mª José, Cerezo Menéndez, Rebeca
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2020
País:España
Institución:Universidad de Burgos (UBU)
Repositorio:Repositorio Institucional de la Universidad de Burgos (RIUBU)
OAI Identifier:oai:riubu.ubu.es:10259/6247
Acceso en línea:http://hdl.handle.net/10259/6247
Access Level:acceso abierto
Palabra clave:Advanced learning technologies
Lifelong learning
Sustainability education
Eye tracking
Data mining techniques
Enseñanza superior
Psicología
Informática
Education, Higher
Psychology
Computer science
Descripción
Sumario: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.