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Meta-Learning: A Nine-Layer Model Based on Metacognition and Smart Technologies

Author

Listed:
  • Athanasios Drigas

    (Net Media Lab & Mind & Brain R&D, N.C.S.R. ‘Demokritos’, 153 41 Agia Paraskevi, Greece)

  • Eleni Mitsea

    (Net Media Lab & Mind & Brain R&D, N.C.S.R. ‘Demokritos’, 153 41 Agia Paraskevi, Greece
    Communication Systems Engineering Department, University of the Aegean, 811 00 Mitilini, Greece)

  • Charalabos Skianis

    (Communication Systems Engineering Department, University of the Aegean, 811 00 Mitilini, Greece)

Abstract

The international organizations of education have already pointed out that the way students learn, what they learn, and the skills needed, will be radically transformed in the coming years. Smart technologies are ready to come into play, changing the conditions of learning, providing opportunities for transformative learning experiences, and promising more conscious, self-directed and self-motivated learning. Meta-learning refers to a set of mental meta-processes by which learners consciously create and manage personal models of learning. Meta-learning entails a cluster of meta-skills that are progressively and hierarchically transformed, ensuring the transition to the highest levels of understanding termed meta-comprehension. The current article aims to investigate the concept of meta-learning and describe the meta-levels of learning through the lens of metacognition. In addition, the potential of smart technologies to provide fertile ground for the implementation of meta-learning training strategies is examined. The results of this article provide a new meta-learning theoretical framework supported by smart devices capable of supporting future meta-learners or, more accurately, meta-thinkers, to transcend the usual states of knowing and move to the next meta-levels of human intelligence.

Suggested Citation

  • Athanasios Drigas & Eleni Mitsea & Charalabos Skianis, 2023. "Meta-Learning: A Nine-Layer Model Based on Metacognition and Smart Technologies," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1668-:d:1036508
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    References listed on IDEAS

    as
    1. Athanasios Drigas & Eleni Mitsea & Charalabos Skianis, 2022. "Virtual Reality and Metacognition Training Techniques for Learning Disabilities," Sustainability, MDPI, vol. 14(16), pages 1-19, August.
    2. Athanasios Drigas & Eleni Mitsea & Charalampos Skianis, 2022. "Subliminal Training Techniques for Cognitive, Emotional and Behavioural Balance. The role of Emerging Technologies," Technium Social Sciences Journal, Technium Science, vol. 33(1), pages 164-186, July.
    3. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
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    Cited by:

    1. Usama M. Ibrahem & Hussein M. Abdelfatah & Dalia M. Kedwany & Abdullah Z. AlMankory & Ibrahem M. Diab & Rabab A. Abdul Kader, 2023. "The Drivers of Change for Future Learning: How Teachers Were Taught in the COVID-19 Crisis and What Will Come Next?," Sustainability, MDPI, vol. 15(20), pages 1-20, October.

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