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

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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)

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.

Suggested Citation

  • María Consuelo Sáiz Manzanares & Juan José Rodríguez Diez & Raúl Marticorena Sánchez & María José Zaparaín Yáñez & Rebeca Cerezo Menéndez, 2020. "Lifelong Learning from Sustainable Education: An Analysis with Eye Tracking and Data Mining Techniques," Sustainability, MDPI, vol. 12(5), pages 1-18, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:5:p:1970-:d:328534
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    References listed on IDEAS

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    1. Naif Radi Aljohani & Ayman Fayoumi & Saeed-Ul Hassan, 2019. "Predicting At-Risk Students Using Clickstream Data in the Virtual Learning Environment," Sustainability, MDPI, vol. 11(24), pages 1-12, December.
    2. Ludovic Seifert & Romain Cordier & Dominic Orth & Yoan Courtine & James L Croft, 2017. "Role of route previewing strategies on climbing fluency and exploratory movements," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-22, April.
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    Cited by:

    1. Pnina Steinberger & Yovav Eshet & Keren Grinautsky, 2021. "No Anxious Student Is Left Behind: Statistics Anxiety, Personality Traits, and Academic Dishonesty—Lessons from COVID-19," Sustainability, MDPI, vol. 13(9), pages 1-18, April.

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