Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of computer & education

Xieling CHEN, Di ZOU, Gary CHENG, Haoran XIE

Research output: Journal PublicationsJournal Article (refereed)

Abstract

Computers & Education has been reporting on the field of computers in education for over 40 years, during which time it has developed into a well-known journal having significant influences on the educational technology research community. Questions such as “what research topics are the academic community of Computers & Education interested in?” “how do such research topics evolve over time?” and “what are the main research concerns for its major contributors?” are important to both the editorial board and readership of Computers & Education. To address these issues, this paper carries out a structural topic modeling analysis of 3963 Computers & Education articles published between 1976 and 2018 bibliometrically. A structural topic model is used to profile the research hotspots of Computers & Education. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas are identified. The major research concerns of articles published in Computers & Education by prolific countries/regions are shown and compared. Thus, this work provides useful insights and implications, and it can be used as a guide for submissions to Computers & Education.
Original languageEnglish
Article number103855
JournalComputers and Education
Volume151
Early online date21 Feb 2020
DOIs
Publication statusE-pub ahead of print - 21 Feb 2020

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Bibliographical note

The research described in this paper has been fully supported by the Standing Committee on Language Education and Research (EDB(LE)/P&R/EL/175/2), the Education Bureau of the Hong Kong Special Administrative Region, the Interdisciplinary Research Scheme of the Dean's Research Fund 2018–19 (FLASS/DRF/IDS-3), Departmental Collaborative Research Fund 2019 (MIT/DCRF-R2/18–19), the Internal Research Grant (RG93/2018-2019R), and the Internal Research Fund (RG 1/2019-2020R), The Education University of Hong Kong, and LEO Dr David P. Chan Institute of Data Science, Lingnan University, Hong Kong..

Keywords

  • Applications in subject areas
  • Data science applications in education

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