Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques
| Autor: | María Consuelo Sáiz Manzanares, Juan José Rodríguez Diez, María José Zaparaín Yáñez, Raúl Marticorena Sánchez, Rebeca Cerezo Menéndez |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Education
Higher Process (engineering) Computer science Geography Planning and Development Lifelong learning lcsh:TJ807-830 lifelong learning data mining techniques lcsh:Renewable energy sources 02 engineering and technology Management Monitoring Policy and Law computer.software_genre eye tracking Task (project management) 0202 electrical engineering electronic engineering information engineering Psychology Enseñanza superior Cluster analysis lcsh:Environmental sciences Informática lcsh:GE1-350 Eye tracking sustainability education Renewable Energy Sustainability and the Environment lcsh:Environmental effects of industries and plants 05 social sciences Perspective (graphical) advanced learning technologies 050301 education Variance (accounting) Advanced learning technologies Psicología Data mining techniques Sustainability education lcsh:TD194-195 020201 artificial intelligence & image processing Data mining 0503 education computer |
| Zdroj: | Sustainability, Vol 12, Iss 5, p 1970 (2020) Scopus Repositorio Institucional de la Universidad de Burgos (RIUBU) instname RUO: Repositorio Institucional de la Universidad de Oviedo Universidad de Oviedo (UNIOVI) RUO. Repositorio Institucional de la Universidad de Oviedo Sustainability Volume 12 Issue 5 |
| ISSN: | 2071-1050 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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