Detecting and visualizing research trends of blended learning: A bibliometric analysis of studies from 2013-2022

Huie CHEN, Daner SUN, Yuqin YANG, Chee Kit LOOI, Fenglin JIA

Research output: Contribution to journalArticlespeer-review

Abstract

Blended learning (BL), an innovative, technology-supported pedagogical approach, has been extensively adopted in schools and universities. The learning effectiveness of BL has been investigated in multiple domains of education, computer science, nursing, engineering, and psychology. To uncover the major trends of BL research, this study embarked on a bibliometric analysis of a total of 719 studies published in the recent 10 years (2013-2022) and indexed in the Web of Science core collection. Adopting a quantitative approach and the visual analytical tool of CiteSpace, the review study identified the development trends, the influential researchers and research institutions, and pivotal studies and topics of the field and informed its future progression. The findings revealed a growing trend in BL research in the past decade as reflected in the exponential growth in the number of publications and citations. Charles R. Graham, Chang Zhu, Robert A. Ellis, and Feifei Han were the most prolific, influential researchers in the field, and the Griffith University, the University of Hong Kong, the Vrije Universiteit Brussel, the Monash University, and the National Taiwan Normal University were the prominent research institutions, which engaged in frequent collaborations with others. The United States, China, and Australia were the top-3 contributors to BL research measured by the number of publications, and the studies conducted and reported by researchers in the USA, Turkey, Taiwan, and Spain were cited the most often. Document co-citation analysis unveiled the pivotal studies and topics of the research field, including blended course designs, institutional adoption, achievement, higher education, active BL, flipped classroom, and communication skills. Copyright © 2023 by the authors; licensee Modestum.

Original languageEnglish
Article numberem2336
JournalEurasia Journal of Mathematics, Science and Technology Education
Volume19
Issue number10
Early online dateAug 2023
DOIs
Publication statusPublished - Oct 2023

Citation

Chen, H., Sun, D., Yang, Y., Looi, C.-K., & Jia, F. (2023). Detecting and visualizing research trends of blended learning: A bibliometric analysis of studies from 2013-2022. Eurasia Journal of Mathematics, Science and Technology Education, 19(10), Article em2336. https://doi.org/10.29333/ejmste/13592

Keywords

  • Blended learning
  • Trend study
  • Bibliometric analysis
  • Data visualization
  • PG student publication

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