The diversity and advance of information, communication, and analytical technologies and their increasing adoption to assist instruction and learning give rise to various technology-driven conferences (e.g., artificial intelligence in education) in educational technology. Previous reviews on educational technology commonly focused on journal articles while seldom including mainstream conference papers which also contribute to an important part of scientific output in computer science and emerging disciplines like educational technology and are equally and even more important than articles in knowledge transmission. Hence, conference papers should also be included in bibliometric studies to produce a complete and precise picture of scientific production concerning educational technology. This study, therefore, uses bibliometrics and topic modeling to analyze papers from mainstream conferences, including Artificial Intelligence in Education, Learning Analytics and Knowledge, Educational Data Mining, Intelligent Tutoring System, and Learning at Scale, focusing on contributors, collaborations, and particularly research topics and topic evolutions to inform relevant stakeholders about educational technology’s development and its future. Results indicate promising areas like affective computing and behavior mining for adaptive instruction, recommender systems in personalized learning recommendations, eye-tracking for cognitive process diagnosis, videos for feedback provision, and natural language processing in discourse analysis and language education. Copyright © 2022 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
CitationChen, X., Zou, D., Xie, H., Chen, G., Lin, J., & Cheng, G. (2023). Exploring contributors, collaborations, and research topics in educational technology: A joint analysis of mainstream conferences. Education and Information Technologies, 28, 1323-1358. doi: 10.1007/s10639-022-11209-y
- Educational technology
- Mainstream conferences
- Structural topic modeling