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.
Bibliographical noteFunding Information:
Governments, for example, those in the UK and the USA, have invested enormously in technologies’ integration into instruction, as witnessed by the growth in funded projects by bodies like the National Science Foundation, Australia Research Council, European Union, and National Natural Science Foundation of China. The effectiveness of various forms of educational devices and applications like chatbots, digital games, and computer-mediated communication attributes mainly to the integrated powerful analytical technologies like data mining and machine learning capable of mining patterns in ever-increasingly educational data and constructing predictive models for better decision making. Therefore, recent decades have witnessed diverse data-driven technologies emerging to support varied learning and instruction tasks, for example, knowledge and skill evaluation, dropout, retention, and academic achievement prediction, students' learning difficulty or misconception diagnosis, automatic grading, feedback, personalized materials provision, and teaching evaluation.
© 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
- Educational technology
- Mainstream conferences
- Structural topic modeling