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Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education

Author

Listed:
  • Qingna Pan

    (School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Jincheng Zhou

    (School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China
    Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun 558000, China
    Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun 558000, China)

  • Duo Yang

    (School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Dingpu Shi

    (School of Computer and Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Dan Wang

    (Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun 558000, China
    Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun 558000, China
    School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Xiaohong Chen

    (School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Jiu Liu

    (Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Province, Duyun 558000, China
    Key Laboratory of Complex Systems and Intelligent Optimization of Qiannan, Duyun 558000, China
    School of Mathematics and Statistics, Qiannan Normal University for Nationalities, Duyun 558000, China)

Abstract

With the rapid development of the global digital knowledge economy, educational activities are facing more challenges. Sustainable development education aims to cultivate students’ thinking ability to better integrate with the contemporary world view, so classroom practice should involve innovative teaching and learning. The goal of sustainable development education is to cultivate talents with high-level thinking and sustainable development abilities. The concept of deep learning emphasizes mobilizing students’ internal motivation, focusing on problem-solving ability, improving students’ critical thinking level, and developing students’ lifelong learning ability. The concept of deep learning has evolved with the times. The introduction of the concept of deep learning in teaching can enhance students’ understanding of the nature of knowledge, cultivate students’ high-level thinking, and enable students to achieve better learning results. Integrating the concept of deep learning into teaching has extremely important significance and value for sustainable development education. It has become a hot topic in the world to comprehensively analyze the research status of deep learning and explore how deep learning can help education achieve sustainable development. In this study, CiteSpace (6.1.R2) visualization analysis software was used to visualize and quantitatively analyze the literature on deep learning in the Social Science Citation Index (SSCI). The visualized analysis is conducted on the annual publication amount, authors, institutions, countries, keywords, and high-frequency cited words of deep learning, to obtain the basic information, development status, hot spots, and evolution trends of deep learning research. The results show that the annual publication volume of deep learning is on the rise; deep learning research has entered a rapid growth stage since 2007; the United States has published the most papers and is the center of the global deep learning research collaboration network; the countries involved in the study were often interconnected, but the institutions and authors were relatively dispersed; research in the field of deep learning mainly focuses on concept exploration, influencing factors, implementation strategies and effectiveness of deep learning; learning method, learning strategy, curriculum design, interactive learning environment are the high-frequency keywords of deep learning research. It can be seen that deep learning research has the characteristics of transnationality, multidisciplinary nature and multi-perspective. In addition, this paper systematically analyzes the latest progress in global deep learning research and objectively predicts that using intelligent technology to design appropriate teaching and learning scenarios and evaluation methods may become the future development trend of deep learning. The research results of this paper will help readers to have a comprehensive understanding of deep learning research, provide deeper and more targeted resources for integrating deep learning concepts into teaching, and promote better sustainable development of education.

Suggested Citation

  • Qingna Pan & Jincheng Zhou & Duo Yang & Dingpu Shi & Dan Wang & Xiaohong Chen & Jiu Liu, 2023. "Mapping Knowledge Domain Analysis in Deep Learning Research of Global Education," Sustainability, MDPI, vol. 15(4), pages 1-22, February.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3097-:d:1062040
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    References listed on IDEAS

    as
    1. Duo Yang & Jincheng Zhou & Dingpu Shi & Qingna Pan & Dan Wang & Xiaohong Chen & Jiu Liu, 2022. "Research Status, Hotspots, and Evolutionary Trends of Global Digital Education via Knowledge Graph Analysis," Sustainability, MDPI, vol. 14(22), pages 1-20, November.
    2. Chaomei Chen, 2006. "CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 359-377, February.
    3. Marta Marsilio & Giulia Cappellaro & Corrado Cuccurullo, 2011. "The Intellectual Structure Of Research Into PPPs," Public Management Review, Taylor & Francis Journals, vol. 13(6), pages 763-782, September.
    4. Dingpu Shi & Jincheng Zhou & Dan Wang & Xiaopeng Wu, 2022. "Research Status, Hotspots, and Evolutionary Trends of Intelligent Education from the Perspective of Knowledge Graph," Sustainability, MDPI, vol. 14(17), pages 1-18, September.
    5. Xiaohong Chen & Jincheng Zhou & Jinqiu Wang & Dan Wang & Jiu Liu & Dingpu Shi & Duo Yang & Qingna Pan, 2022. "Visualizing Status, Hotspots, and Future Trends in Mathematical Literacy Research via Knowledge Graph," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
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