Twenty Years of Personalized Language learning : Topic Modeling and Knowledge Mapping

Xieling CHEN, Di ZOU*, Haoran XIE, Gary CHENG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

69 Citations (Scopus)


Personalized language learning (PLL), a popular approach to precision language education, plays an increasingly essential role in effective language education to meet diverse learner needs and expectations. Research on PLL has become an active sub-field of research on technology-enhanced language learning and artificial intelligence applications in education. Based on the PLL literature from the Web of Science and Scopus databases, this study identified trends and prominent research issues within the field from 2000 to 2019 using structural topic modeling and bibliometrics. Trend analysis of articles demonstrated increasing interest in PLL research. Journals such as Educational Technology & Society and Computers & Education had contributed much to PLL research. PLL associated closely with mobile learning, game-based learning, and online/web-based learning. Moreover, personalized feedback and recommendations were important issues in PLL. Additionally, there was an increasing interest in adopting learning analytics and artificial intelligence in PLL research. Results obtained could help practitioners and scholars better understand the trends and status of PLL research and become aware of the hot topics and future directions.
Original languageEnglish
Pages (from-to)205-222
Number of pages18
JournalEducational Technology and Society
Issue number1
Publication statusPublished - Jan 2021

Bibliographical note

This research was supported by the Faculty Research Fund (102041) and the Lam Woo Research Fund (LWI20011) of Lingnan University, Hong Kong, the One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20), the Research Cluster Fund (RG 78/2019-2020R), and the Interdisciplinary Research Scheme of the Dean’s Research Fund 2019-20 (FLASS/DRF/IDS-2) of The Education University of Hong Kong, Hong Kong.


  • Personalized language learning
  • Topic modeling
  • Knowledge mapping
  • Bibliometrics
  • Precision education


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