IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1668-d1036508.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1668/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1668/
    Download Restriction: no
    ---><---

    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. repec:thr:techub:10033:y:2022:i:1:p:164-186 is not listed on IDEAS
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.
    2. Xi Liu & Yugang He & Renhong Wu, 2024. "Revolutionizing Environmental Sustainability: The Role of Renewable Energy Consumption and Environmental Technologies in OECD Countries," Energies, MDPI, vol. 17(2), pages 1-21, January.
    3. Gianluca MISURACA & Colin van Noordt, 2020. "AI Watch - Artificial Intelligence in public services: Overview of the use and impact of AI in public services in the EU," JRC Research Reports JRC120399, Joint Research Centre.
    4. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    5. Sergio Genovesi & Julia Maria Mönig, 2022. "Acknowledging Sustainability in the Framework of Ethical Certification for AI," Sustainability, MDPI, vol. 14(7), pages 1-10, March.
    6. Tan Yigitcanlar & Rashid Mehmood & Juan M. Corchado, 2021. "Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures," Sustainability, MDPI, vol. 13(16), pages 1-14, August.
    7. Ricardo Vinuesa & Soledad Le Clainche, 2022. "Machine-Learning Methods for Complex Flows," Energies, MDPI, vol. 15(4), pages 1-5, February.
    8. Qian, Yu & Xu, Zeshui & Qin, Yong & Gou, Xunjie & Skare, Marinko, 2023. "Measuring the varying relationships between sustainable development and oil booms in different contexts: An empirical study," Resources Policy, Elsevier, vol. 85(PB).
    9. Krzysztof Rusek & Agnieszka Kleszcz & Albert Cabellos-Aparicio, 2022. "Bayesian inference of spatial and temporal relations in AI patents for EU countries," Papers 2201.07168, arXiv.org.
    10. Hanna Obracht-Prondzyńska & Ewa Duda & Helena Anacka & Jolanta Kowal, 2022. "Greencoin as an AI-Based Solution Shaping Climate Awareness," IJERPH, MDPI, vol. 19(18), pages 1-25, September.
    11. Aras Bozkurt & Abdulkadir Karadeniz & David Baneres & Ana Elena Guerrero-Roldán & M. Elena Rodríguez, 2021. "Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century," Sustainability, MDPI, vol. 13(2), pages 1-16, January.
    12. Hernandez-Matheus, Alejandro & Löschenbrand, Markus & Berg, Kjersti & Fuchs, Ida & Aragüés-Peñalba, Mònica & Bullich-Massagué, Eduard & Sumper, Andreas, 2022. "A systematic review of machine learning techniques related to local energy communities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    13. Olga Pilipczuk, 2020. "Sustainable Smart Cities and Energy Management: The Labor Market Perspective," Energies, MDPI, vol. 13(22), pages 1-24, November.
    14. Raghu Raman & Krishnashree Achuthan & Ricardo Vinuesa & Prema Nedungadi, 2021. "COVIDTAS COVID-19 Tracing App Scale—An Evaluation Framework," Sustainability, MDPI, vol. 13(5), pages 1-19, March.
    15. Jon Truby, 2020. "Governing Artificial Intelligence to benefit the UN Sustainable Development Goals," Sustainable Development, John Wiley & Sons, Ltd., vol. 28(4), pages 946-959, July.
    16. Arasteh, Mohammad Ali & Farjami, Yaghoub, 2021. "Supporting Sustainable Rural Groundwater Demand Management with Fuzzy Decision Analysis: A Case Study in Iran," Utilities Policy, Elsevier, vol. 70(C).
    17. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    18. Olga Pilipczuk, 2021. "Determinants of Managerial Competences Transformation in the Polish Energy Industry," Energies, MDPI, vol. 14(20), pages 1-27, October.
    19. Naudé, Wim & Dimitri, Nicola, 2021. "Public Procurement and Innovation for Human-Centered Artificial Intelligence," IZA Discussion Papers 14021, Institute of Labor Economics (IZA).
    20. Nadia Ahmed & Marco Levorato & Roberto Valentini & Guann-Pyng Li, 2020. "Data Driven Optimization of Energy Management in Residential Buildings with Energy Harvesting and Storage," Energies, MDPI, vol. 13(9), pages 1-18, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1668-:d:1036508. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.