Past, present, and future of smart learning: a topic-based bibliometric analysis

Xieling CHEN, Di ZOU*, Haoran XIE, Fu Lee WANG

*Corresponding author for this work

Research output: Journal PublicationsReview articleOther Review

89 Citations (Scopus)


Innovative information and communication technologies have reformed higher education from the traditional way to smart learning. Smart learning applies technological and social developments and facilitates effective personalized learning with innovative technologies, especially smart devices and online technologies. Smart learning has attracted increasing research interest from the academia. This study aims to comprehensively review the research field of smart learning by conducting a topic modeling analysis of 555 smart learning publications collected from the Scopus database. In particular, it seeks answers to (1) what the major research topics concerning smart learning were, and (2) how these topics evolved. Results demonstrate several major research issues, for example, Interactive and multimedia learning, STEM (science, technology, engineering, and mathematics) education, Attendance and attention recognition, Blended learning for smart learning, and Affective and biometric computing. Furthermore, several emerging topics were identified, for example, Smart learning analytics, Software engineering for e-learning systems, IoT (Internet of things) and cloud computing, and STEM education. Additionally, potential inter-topic directions were highlighted, for instance, Attendance and attention recognition and IoT and cloud computing, Semantics and ontology and Mobile learning, Feedback and assessment and MOOCs (massive open online courses) and course content management, as well as Blended learning for smart learning and Ecosystem and ambient intelligence.
Original languageEnglish
Article number2
JournalInternational Journal of Educational Technology in Higher Education
Issue number1
Early online date15 Jan 2021
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
This work was supported by the Research Seed Fund (102367) and Direct Grant (101138) at Lingnan University, Hong Kong, and the Internal Research Fund at The Education University of Hong Kong (RG1/19-20R), Hong Kong.

Publisher Copyright:
© 2021, The Author(s).


  • Research hotspots
  • Smart learning
  • Topic evolution
  • Topic modeling


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