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Cited by 11Innovations in Medical EducationE-Learning and COVID-19Education during COVID-19 pandemic

The impact of COVID-19 on online medical education: a knowledge graph analysis based on co-term analysis

Open access
Deng, Huijiao, Jiang, Yi, Han, Qinrong, Zhou, Xingyu, Zhong, Siyang, Hu, Kai, Yang, Lin·2023·BMC Medical Education
DOI 10.1186/s12909-023-04193-5PMC PMC10067015

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Abstract

Background: This study aims to identify the characteristics and future directions of online medical education in the context of the novel coronavirus outbreak new through visual analytics using CiteSpace and VOSviewer bibliometric methods. Method: From Web of Science, we searched for articles published between 2020 and 2022 using the terms online education, medical education and COVID-19, ended up with 2555 eligible papers, and the articles published between 2010 and 2019 using the terms online education, medical education and COVID-19, and we ended up with 4313 eligible papers. Results: Before the COVID-19 outbreak, Medical students and care were the most frequent keywords and the most cited author was BRENT THOMA with 18 times. The United States is the country with the greatest involvement and research impact in the field of online medical education. The most cited journal is ACAD MED with 1326 citations. After the COVID-19 outbreak, a surge in the number of research results in related fields, and ANXIETY and four secondary keywords were identified. In addition, the concentration of authors of these publications in the USA and China is a strong indication that local epidemics and communication technologies have influenced the development of online medical education research. Regarding the centrality of research institutions, the most influential co-author network is Harvard Medical School in the United States; and regarding the centrality of references, the most representative journal to which it belongs is VACCINE. Conclusion: This study found that hey information such as keywords, major institutions and authors, and countries differ in the papers before and after the COVID-19 outbreak. The novel coronavirus outbreak had a significant impact on the online education aspect. For non-medical and medical students, the pandemic has led to home isolation, making it difficult to offer face-to-face classes such as laboratory operations. Students have lost urgency and control over the specifics of face-to-face instruction, which has reduced the quality of teaching. Therefore, we should improve our education model according to the actual situation to ensure the quality of teaching while taking into account the physical and psychological health of students.

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  • Knowledge graphs can help learners move beyond isolated memorization and visualize how different concepts are connected, thereby facilitating knowledge organization, cognitive mapping, and conceptual integration.

    “By making such relationships explicit, knowledge graphs can help learners move beyond isolated memorization and visualize how different concepts are connected, thereby facilitating knowledge organization, cognitive mapping, and conceptual integration [14].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [14]

    The Method (Research overview; page unavailable) describes visualizing relationships among co-occurring subject terms, which supports the claim that a graph can make connections visible. It uses those graphs to analyze research literature, however; it does not say that learners use them to move beyond memorization or improve knowledge organization, cognitive mapping, or conceptual integration. (LLM, conf=0.79)

    Experimental — accuracy not yet measured

  • Knowledge graphs provide a structured visual representation of relationships among concepts, enabling students to organize knowledge more effectively.

    “Knowledge graphs provide a structured visual representation of relationships among concepts, enabling students to organize knowledge more effectively [12, 14].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [14]

    The Method (Research overview; page unavailable) describes visual maps of relationships among co-occurring terms. The paper uses these maps to analyze research literature, however, and does not say that they help students organize their knowledge more effectively. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The integration of AI tutoring with knowledge graphs supports structured knowledge construction.

    “Third, the integration of AI tutoring with knowledge graphs supports structured knowledge construction [14, 23].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [14]

    The Method (Research overview; page unavailable) describes bibliometric keyword networks made with CiteSpace and VOSviewer, not knowledge graphs used for tutoring. Searches for AI tutoring, artificial intelligence, and structured knowledge construction found no passage addressing whether their integration supports learning. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

  • The results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs.

    “Second, the results highlight the importance of combining AI technologies with structured knowledge representation tools, such as knowledge graphs [12, 13, 14].”

    From Effectiveness of a generative AI-powered digital tutor integrated with a knowledge graph in anatomy education for nursing students: a randomized controlled trial (2026) · cites this paper as [14]

    The Method (Research overview; page unavailable) describes using CiteSpace and VOSviewer to visualize bibliometric networks. Searches for AI, artificial intelligence, and integration with knowledge graphs found no passage in our copy saying that combining them is important. (LLM, conf=0.73)

    Experimental — accuracy not yet measured

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Cites

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Research works printed but not linked: 30

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Cited by

Sorted by citations

Authors
Deng, H., Jiang, Y., Han, Q., Zhou, X., Zhong, S., Hu, K., & Yang, L.
Affiliations
  • Zunyi Medical University (CN)
  • Chinese Academy of Sciences (CN)
Corresponding author
  • Hu, Kai
  • Yang, Lin
ORCID
Venue
BMC Medical Education
Publisher
Springer Science and Business Media LLC
Published
2023-04-01
Volume
23
Issue
1
Pages
203-203
ISSN
1472-6920 (ISSN-L)
DOI
10.1186/s12909-023-04193-5
PMC
PMC10067015
OpenAlex
W4362522295
Cited by
11
References
2
Citations by year
2026: 3 · 2025: 3 · 2024: 3 · 2023: 2
FWCI
2.82
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Medical Education (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • the College Students Innovation and Entrepreneurship Training Program of Zunyi Medical University (No.ZHCX2022002, No.ZHCX2022003)
  • National Innovation and Entrepreneurship Training Program for College Students (No.202210661029)
  • Cooperative Education Project of Production and Education of the Higher Education Department of the Ministry of Education (No.202101250012)
  • National Natural Science Foundation of China (No.82260456)
  • The Undergraduate education and teaching reform plan project of Zunyi Medical University (No.XJJG2022-95)
Topics
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
  • Social Sciences › Education › E-Learning and COVID-19
  • Social Sciences › Education › Education during COVID-19 pandemic
Keywords
online medical education, COVID-19 pandemic, medical students, online learning, research trends, co-authorship networks, teaching quality, student mental health
MeSH terms
COVID-19, SARS-CoV-2, Education, Medical, Humans, Pattern Recognition, Automated, Education, Distance

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