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@article{Bai2020,
title = {Visualizing research trends and research theme evolution in E‐learning field: 1999–2018},
author = {Yang Bai and Hongxiu Li and Yong Liu},
url = {https://link.springer.com/article/10.1007/s11192-020-03760-7},
doi = {https://doi.org/10.1007/s11192-020-03760-7},
year = {2020},
date = {2020-11-19},
journal = {Scientometrics},
abstract = {This paper aims to provide a comprehensive understanding of the evolution of major research themes and trends in e-learning research. A co-word analysis is applied for the analysis of the 21,656 keywords collected from 7214 articles published in 10 journals in the field of e-learning from the years 1999 to 2018. Specifically, a cluster analysis, social network analysis, strategic diagram, and graph theory were applied in the analysis for two time periods: 1999–2008 and 2009–2018. The study detects the bridging, popular, and core topics in e-learning research for the two periods. The research results indicate that e-learning research has undergone a health evolution over the past two decades. There is a temporal continuity of e-learning research because some research topics have kept their continuity over the studied 20 years. Meanwhile, the research traditions in the e-learning field are also continuously evolving with the development of new technologies. The results also offer useful hints on the future direction of how the field may evolve.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}

@article{Deng2020,
title = {Exploring the topic structure and evolution of associations in information behavior research through co-word analysis},
author = {Shengli Deng and Sudi Xia and Jiming Hu and Hongxiu Li and Yong Liu },
doi = {https://doi.org/10.1177/0961000620938120},
year = {2020},
date = {2020-07-01},
journal = {Journal of Librarianship and Information Science},
abstract = {This study aims to reveal the distribution of topics, and the associations among them, in information behavior research from 2009 to 2018. Working with a collection of 6744 publications from the Web of Science database, co-word analysis is used to investigate the overall topic structure, the associations among the topics, and their evolution in different years, which is supplemented by visualization with science maps. The results uncovered an unbalanced distribution of topics, and that the topics cluster into six communities representing subdivisions of this field: information behavior in patient-centered studies; information interaction in the digital environment; information literacy in health and academic contexts; health literacy on the Internet; information behavior in child-centered studies; and information behavior in medical informatics. The findings supplement and provide refinements to work on the state of this field, and help researchers obtain an overview of the past decade to guide their future work.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}