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

• Published in: Sustainability Vol. 12; no. 5; p. 1970
• Main Authors: Sáiz Manzanares, María Consuelo, Rodríguez Diez, Juan José, Marticorena Sánchez, Raúl, Zaparaín Yáñez, María José, Cerezo Menéndez, Rebeca
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2020
• Subjects: Algorithms; Clustering; Cognition & reasoning; Cognitive tasks; Education; Environmental education; Eye movements; Knowledge; Learning; Lifelong learning; Machine learning; Metacognition; Methods; Multimedia; Reading comprehension; Statistical analysis; Sustainability; Technology assessment; Tutoring; Visualization
• ISSN: 2071-1050; 2071-1050
• DOI: 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.