A decade of learning analytics: Structural topic modeling based bibliometric analysis

Xieling CHEN, Di ZOU*, Haoran XIE

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Learning analytics (LA) has become an increasingly active field focusing on leveraging learning process data to understand and improve teaching and learning. With the explosive growth in the number of studies concerning LA, it is significant to investigate its research status and trends, particularly the thematic structure. Based on 3900 LA articles published during the past decade, this study explores answers to questions such as “what research topics were the LA community interested in?” and “how did such research topics evolve?” by adopting structural topic modeling and bibliometrics. Major publication sources, countries/regions, institutions, and scientific collaborations were examined and visualized. Based on the analyses, we present suggestions for future LA research and discussions about important topics in the field. It is worth highlighting LA combining various innovative technologies (e.g., visual dashboards, neural networks, multimodal technologies, and open learner models) to support classroom orchestration, personalized recommendation/feedback, self-regulated learning in flipped classrooms, interaction in game-based and social learning. This work is useful in providing an overview of LA research, revealing the trends in LA practices, and suggesting future research directions.
Original languageEnglish
Pages (from-to)10517-10561
Number of pages45
JournalEducation and Information Technologies
Volume27
Issue number8
Early online date18 Apr 2022
DOIs
Publication statusPublished - Sep 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Keywords

  • Learning analytics
  • Research topics
  • Topic evolution
  • Structural topic modeling
  • Social network analysis

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