Detecting latent topics and trends in educational technologies over four decades using structural topic modeling: A retrospective of all volumes of Computers & Education. (July 2020)

• Record Type: Journal Article
• Authors: Chen, Xieling; Zou, Di; Cheng, Gary; Xie, Haoran
• Is Part Of: Computers & education. Volume 151(2020)
• Publication Date: 2020-07
• Subjects: Applications in subject areas -- Data science applications in education; Education -- Data processing -- Periodicals; Education -- Periodicals; Computers -- Periodicals; Computer-Assisted Instruction -- Periodicals; Éducation -- Informatique -- Périodiques; Electronic journals; 370.285
• DOI: 10.1016/j.compedu.2020.103855
• Languages: English
• ISSNs: 0360-1315

Abstract

Abstract: Computers & Education has been leading the field of computers in education for over 40 years, during which time it has developed into a well-known journal with significant influences on the educational technology research community. Questions such as "in what research topics were the academic community of Computers & Education interested?" "how did such research topics evolve over time?" and "what were the main research concerns of its major contributors?" are important to both the editorial board and readership of Computers & Education . To address these issues, this paper conducted a structural topic modeling analysis of 3963 articles published in Computers & Education between 1976 and 2018 bibliometrically. A structural topic model was used to profile the research hotspots. By further exploring annual topic proportion trends and topic correlations, potential future research directions and inter-topic research areas were identified. The major research concerns of the publications in Computers & Education by prolific countries/regions were shown and compared. Thus, this work provided useful insights and implications, and it could be used as a guide for contributors to Computers & Education . Highlights: Conduct a structural topic modeling based analysis of Computers & Education. Identify major contributors and visualize the scientific collaborations. Identify predominant research topics and potential future directions. Visualize topical distributions of major contributors.