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

María Consuelo Sáiz Manzanares, Juan José Rodríguez Diez, Raúl Marticorena Sánchez, María José Zaparaín Yáñez and Rebeca Cerezo Menéndez
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María Consuelo Sáiz Manzanares: Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, Research Group DATAHES, Pº Comendadores s/n, 09001 Burgos, Spain
Juan José Rodríguez Diez: Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avd. de Cantabria s/n, 09006 Burgos, Spain
Raúl Marticorena Sánchez: Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Research Group ADMIRABLE, Escuela Politécnica Superior, Avd. de Cantabria s/n, 09006 Burgos, Spain
María José Zaparaín Yáñez: Departamento de Historia, Geografía y Comunicación, Facultad de Humanidades y Comunicación, Universidad de Burgos, Research Group PART, Pº Comendadores s/n, 09001 Burgos, Spain
Rebeca Cerezo Menéndez: Departamento de Psicología, Facultad de Psicología, Universidad de Oviedo, Research Group ADIR, Plaza de Feijoo, 33003 Oviedo, Asturias, Spain

Sustainability, 2020, vol. 12, issue 5, 1-18

Abstract: 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.

Keywords: advanced learning technologies; lifelong learning; sustainability education; eye tracking; data mining techniques (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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