2026-06-21T18:10:05Zhttps://riubu.ubu.es/oai/request

oai: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

00925njm 22002777a 4500 dc Sáiz Manzanares, María Consuelo author Rodríguez Diez, Juan José author Marticorena Sánchez, Raúl author Zaparaín Yáñez, Mª José author Cerezo Menéndez, Rebeca author 2020-03 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. 2071-1050 http://hdl.handle.net/10259/6247 10.3390/su12051970 Advanced learning technologies Lifelong learning Sustainability education Eye tracking Data mining techniques Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques