Bibliometric analysis of the application of self-directed learning in medical education

doi:10.3760/cma.j.cn115259-20251013-01276

Abstract

Abstract: Objective To analyze the current status and emerging frontiers of selfdirected learning applications in the medical field. Methods A bibliometric approach was adopted. The publications on the application of selfdirected learning in medicine from January 1, 2015 to December 31, 2024 in the Web of Science Core Collection, China National Knowledge Infrastructure, Wanfang Data, and Chinese Medical Journals databases was retrieved. VOSviewer 1.6.18 was used for keyword clustering analysis, and CiteSpace 6.3.R1 was used for burst keyword detection. Results A total of 2 523 Chinese-language and 932 English-language publications were included. The overall annual publication volume of Chinese articles was higher than that of English articles; however, from 2020, the annual number of English publications increased markedly. Highfrequency keywords from Chinese and English literatures formed six and four clusters, respectively. Prominent burst keywords included ″selfassessment″, ″artificial intelligence″, ″clinical thinking″ and ″teaching evaluation.″ Conclusions Research on self-directed learning in medical education has continued to gain momentum. While Chinese-language publications dominate in total volume, English-language research has exhibited a sharper growth trajectory in recent years. The research hotspots center on the application of emerging technologies, the establishment of teaching evaluation systems, and the cultivation of clinical thinking, all of which have implications for future research directions.

Key words: Bibliometric, Self-directed learning, Medical education, Application status, Emerging frontiers

CLC Number:

R-05

Liu Huiting, Huang Xiaoming, Fan Hongwei, Jiao Yang. Bibliometric analysis of the application of self-directed learning in medical education[J]. Chinese Journal of Medical Education, 2026, 46(9): 672-677, DOI: 10.3760/cma.j.cn115259-20251013-01276.

References

[1] 李海潮, 齐心, 吴红斌. 医学教育全过程视角下院校教育阶段教学改革的思考与实践[J].中华医学教育杂志,2023,43(2):81-85. DOI: 10.3760/cma.j.cn115259-20220615-00787.
[2] Rajalakshmi M, Ganapathy K. Effect of self-directed learning module and assessment on learning of national health programme by medical undergraduates-a mixed methods evaluation[J]. Indian J Community Med, 2023,48(3):465-470. DOI: 10.4103/ijcm.ijcm_520_22.
[3] 石立莹, 闫景瑞, 李咏梅. 基于基础医学知识图谱的自主学习教学设计[J].基础医学教育,2023,25(10):862-865. DOI: 10.13754/j.issn2095-1450.2023.10.06.
[4] 崔妍, 谢克亮. 基于案例学习联合床旁教学模式在重症医学教学中的应用[J].中华医学教育杂志,2024,44(12):924-928. DOI: 10.3760/cma.j.cn115259-20240503-00448.
[5] 黎秀婵, 雷曼婷, 方建冰, 等. 翻转课堂在护理教学查房中对学生自主学习能力的影响[J].中国继续医学教育,2024,16(9):90-95. DOI: 10.3969/j.issn.1674-9308.2024.09.018.
[6] 李翠薇, 杨林. 创新继续医学教育学习模式激发自主学习主动性[J].中国继续医学教育,2021,13(31):141-144. DOI: 10.3969/j.issn.1674-9308.2021.31.036.
[7] 蒋伟燕, 赖媚媚, 林潮庆, 等. 案例与问题导向学习双轨教学模式在检验医学科住院医师规范化培训中的应用[J].中华检验医学杂志,2022,45(11):1177-1181. DOI: 10.3760/cma.j.cn114452-20211225-00795.
[8] Trullàs JC, Blay C, Sarri E, et al. Effectiveness of problem-based learning methodology in undergraduate medical education: a scoping review[J]. BMC Med Educ, 2022,22(1):104. DOI: 10.1186/s12909-022-03154-8.
[9] van Houten-Schat MA, Berkhout JJ, van Dijk N, et al. Self-regulated learning in the clinical context: a systematic review[J]. Med Educ, 2018,52(10):1008-1015. DOI: 10.1111/medu.13615.
[10] Zhang Y, Xie X, Xu Q. ChatGPT in medical education: bibliometric and visual analysis[J]. JMIR Med Educ, 2025,11:e72356. DOI: 10.2196/72356.
[11] Chen C. A glimpse of the first eight months of the COVID-19 literature on microsoft academic graph: themes, citation contexts, and uncertainties[J]. Front Res Metr Anal, 2020,5:607286. DOI: 10.3389/frma.2020.607286.
[12] 刘慧婷, 李玥, 焦洋, 等. 临床环境下影响住院医师自主学习能力及效率相关因素的研究进展[J].中国毕业后医学教育,2025,9(7):509-512. DOI: 10.3969/j.issn.2096-4293.2025.07.008.
[13] Tayem YI. The impact of small group case-based learning on traditional pharmacology teaching[J]. Sultan Qaboos Univ Med J, 2013,13(1):115-120. DOI: 10.12816/0003204.
[14] 卢政扬, 邢力刚. 美国住院医师规范化培训胜任力体系的借鉴与思考[J].社区医学杂志,2022,20(10):574-577,582. DOI: 10.19790/j.cnki.JCM.2022.10.10.
[15] 孟煜, 夏人青. 美国专科医师培训制度体系探析及启示[J].复旦教育论坛,2020,18(2):99-105. DOI: 10.3969/j.issn.1672-0059.2020.02.017.
[16] Zheng B, He Q, Lei J. Informing factors and outcomes of self-assessment practices in medical education: a systematic review[J]. Ann Med, 2024,56(1):2421441. DOI: 10.1080/07853890.2024.2421441.
[17] Alizadeh M, Jafar Sameri M. Intelligent assessment systems in medical education: a systematic review[J]. J Adv Med Educ Prof, 2025,13(3):173-190. DOI: 10.30476/jamp.2024.104560.2068.
[18] Marcotte K, Negrete Manriquez JA, Hunt M, et al. Trainees′ perspectives on the next era of assessment and precision education[J]. Acad Med, 2024,99(4S Suppl 1):S25-S29. DOI: 10.1097/ACM.0000000000005602.
[19] Rodda J, Mansi H, Fernando-Sayers J, et al. Virtual and augmented reality in undergraduate medical education in psychiatry: a systematic review[J]. Clin Teach, 2025,22(4):e70128. DOI: 10.1111/tct.70128.
[20] 张妍馨, 薄红, 郭庆峰, 等. 教育数字化背景下构建数智化医学教育质量保障体系的思考[J].中华医学教育杂志,2025,45(8):567-570. DOI: 10.3760/cma.j.cn115259-20241225-01354.
[21] Boscardin CK, Gin B, Golde PB, et al. ChatGPT and generative artificial intelligence for medical education: potential impact and opportunity[J]. Acad Med, 2024,99(1):22-27. DOI: 10.1097/ACM.0000000000005439.
[22] Ng F, Thirunavukarasu AJ, Cheng H, et al. Artificial intelligence education: an evidence-based medicine approach for consumers, translators, and developers[J]. Cell Rep Med, 2023,4(10):101230. DOI: 10.1016/j.xcrm.2023.101230.
[23] 商杰森, 程怀志, 郭斌, 等. 线上线下混合式教学在我国医学教学改革的研究热点和趋势分析[J].中国卫生事业管理,2023,40(4):302-305.