IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v11y2024i1d10.1057_s41599-024-02717-y.html
   My bibliography  Save this article

Education reform and change driven by digital technology: a bibliometric study from a global perspective

Author

Listed:
  • Chengliang Wang

    (Zhejiang University of Technology)

  • Xiaojiao Chen

    (Zhejiang University of Technology)

  • Teng Yu

    (Universiti Sains Malaysia
    The Chinese University of Hong Kong)

  • Yidan Liu

    (Zhejiang University of Technology
    Beihang University)

  • Yuhui Jing

    (Zhejiang University of Technology)

Abstract

Amidst the global digital transformation of educational institutions, digital technology has emerged as a significant area of interest among scholars. Such technologies have played an instrumental role in enhancing learner performance and improving the effectiveness of teaching and learning. These digital technologies also ensure the sustainability and stability of education during the epidemic. Despite this, a dearth of systematic reviews exists regarding the current state of digital technology application in education. To address this gap, this study utilized the Web of Science Core Collection as a data source (specifically selecting the high-quality SSCI and SCIE) and implemented a topic search by setting keywords, yielding 1849 initial publications. Furthermore, following the PRISMA guidelines, we refined the selection to 588 high-quality articles. Using software tools such as CiteSpace, VOSviewer, and Charticulator, we reviewed these 588 publications to identify core authors (such as Selwyn, Henderson, Edwards), highly productive countries/regions (England, Australia, USA), key institutions (Monash University, Australian Catholic University), and crucial journals in the field (Education and Information Technologies, Computers & Education, British Journal of Educational Technology). Evolutionary analysis reveals four developmental periods in the research field of digital technology education application: the embryonic period, the preliminary development period, the key exploration, and the acceleration period of change. The study highlights the dual influence of technological factors and historical context on the research topic. Technology is a key factor in enabling education to transform and upgrade, and the context of the times is an important driving force in promoting the adoption of new technologies in the education system and the transformation and upgrading of education. Additionally, the study identifies three frontier hotspots in the field: physical education, digital transformation, and professional development under the promotion of digital technology. This study presents a clear framework for digital technology application in education, which can serve as a valuable reference for researchers and educational practitioners concerned with digital technology education application in theory and practice.

Suggested Citation

  • Chengliang Wang & Xiaojiao Chen & Teng Yu & Yidan Liu & Yuhui Jing, 2024. "Education reform and change driven by digital technology: a bibliometric study from a global perspective," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-17, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02717-y
    DOI: 10.1057/s41599-024-02717-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-024-02717-y
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-024-02717-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author-based citation analysis?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    2. Teng Yu & Jian Dai & Chengliang Wang, 2023. "Adoption of blended learning: Chinese university students’ perspectives," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    3. Andreas Strotmann & Dangzhi Zhao, 2012. "Author name disambiguation: What difference does it make in author‐based citation analysis?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(9), pages 1820-1833, September.
    4. Satish Kumar & Weng Marc Lim & Nitesh Pandey & J. Christopher Westland, 2021. "20 years of Electronic Commerce Research," Electronic Commerce Research, Springer, vol. 21(1), pages 1-40, March.
    5. Krishnamurthy, Sandeep, 2020. "The future of business education: A commentary in the shadow of the Covid-19 pandemic," Journal of Business Research, Elsevier, vol. 117(C), pages 1-5.
    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. Qi Tan & Zhiqiang Yuan, 2024. "A professional development course inviting changes in preservice mathematics teachers’ integration of technology into teaching: the lens of instrumental orchestration," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    2. Haijie Wang & Le Yang & Yanchao Feng, 2024. "How does digital economy affect the industry chain resilience in China?," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.

    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. Xianru Shang & Zijian Liu & Chen Gong & Zhigang Hu & Yuexuan Wu & Chengliang Wang, 2024. "Knowledge mapping and evolution of research on older adults’ technology acceptance: a bibliometric study from 2013 to 2023," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-21, December.
    2. Mike Thelwall, 2020. "Mid-career field switches reduce gender disparities in academic publishing," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(3), pages 1365-1383, June.
    3. Dangzhi Zhao & Andreas Strotmann, 2020. "Telescopic and panoramic views of library and information science research 2011–2018: a comparison of four weighting schemes for author co-citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 255-270, July.
    4. Kim, Jinseok & Diesner, Jana, 2015. "The effect of data pre-processing on understanding the evolution of collaboration networks," Journal of Informetrics, Elsevier, vol. 9(1), pages 226-236.
    5. Mi Zhou & Biyu Bian & Weiming Zhu & Li Huang, 2021. "A Half Century of Research on Childhood and Adolescent Depression: Science Mapping the Literature, 1970 to 2019," IJERPH, MDPI, vol. 18(18), pages 1-20, September.
    6. Ciriaco Andrea D’Angelo & Nees Jan Eck, 2020. "Collecting large-scale publication data at the level of individual researchers: a practical proposal for author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 883-907, May.
    7. Jinseok Kim & Jinmo Kim & Jason Owen-Smith, 2019. "Generating automatically labeled data for author name disambiguation: an iterative clustering method," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 253-280, January.
    8. Liu, Meijun & Hu, Xiao, 2021. "Will collaborators make scientists move? A Generalized Propensity Score analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    9. Lutz Bornmann & Werner Marx, 2014. "How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 487-509, January.
    10. Xuan Zhen Liu & Hui Fang, 2014. "Scientific group leaders’ authorship preferences: an empirical investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 909-925, February.
    11. Freeman, Richard B. & Huang, Wei, 2014. "Collaborating With People Like Me: Ethnic Co-authorship within the US," IZA Discussion Papers 8432, Institute of Labor Economics (IZA).
    12. Uyen-Phuong Nguyen & Philip Hallinger, 2020. "Assessing the Distinctive Contributions of Simulation & Gaming to the Literature, 1970-2019: A Bibliometric Review," Simulation & Gaming, , vol. 51(6), pages 744-769, December.
    13. Jinseok Kim, 2019. "A fast and integrative algorithm for clustering performance evaluation in author name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 661-681, August.
    14. Xie, Qing & Zhang, Xinyuan & Song, Min, 2021. "A network embedding-based scholar assessment indicator considering four facets: Research topic, author credit allocation, field-normalized journal impact, and published time," Journal of Informetrics, Elsevier, vol. 15(4).
    15. Jinseok Kim & Liang Tao & Seok-Hyoung Lee & Jana Diesner, 2016. "Evolution and structure of scientific co-publishing network in Korea between 1948–2011," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 27-41, April.
    16. Giovanni Colavizza & Iain Hrynaszkiewicz & Isla Staden & Kirstie Whitaker & Barbara McGillivray, 2020. "The citation advantage of linking publications to research data," PLOS ONE, Public Library of Science, vol. 15(4), pages 1-18, April.
    17. Peng Bao & Chengxiang Zhai, 2017. "Dynamic credit allocation in scientific literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 595-606, July.
    18. Richard B. Freeman & Wei Huang, 2015. "Collaborating with People Like Me: Ethnic Coauthorship within the United States," Journal of Labor Economics, University of Chicago Press, vol. 33(S1), pages 289-318.
    19. Jinseok Kim & Jenna Kim & Jason Owen‐Smith, 2021. "Ethnicity‐based name partitioning for author name disambiguation using supervised machine learning," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(8), pages 979-994, August.
    20. Carlo Dindorf & Eva Bartaguiz & Freya Gassmann & Michael Fröhlich, 2022. "Conceptual Structure and Current Trends in Artificial Intelligence, Machine Learning, and Deep Learning Research in Sports: A Bibliometric Review," IJERPH, MDPI, vol. 20(1), pages 1-23, December.

    More about this item

    Statistics

    Access and download statistics

    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:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02717-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.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.