The advance of educational technologies and digital devices have made computer-assisted language learning (CALL) an active interdisciplinary field with increasing research potential and topic diversity. Questions like “what topics and technologies attract the interest of the CALL community?,” “how have these topics and technologies evolved?,” and “what is the future of CALL?” are key to understanding where the CALL field has been and where it is going. To help answer these questions, the present review combined structural topic modeling, the Mann-Kendall trend test, and hierarchical clustering with bibliometrics to investigate the research status, trends, and prominent issues in CALL from 1,295 articles over the past 25 years ending in 2020. Major findings revealed that Social Sciences Citation Indexed journals such as Computer Assisted Language Learning, Language Learning & Technology, and ReCALL contributed most to the field. Topics that drew the most interest included mobile-assisted language learning, project-based learning, and blended learning. Topics drawing increasing research interest include mobile-assisted language learning, seamless learning, wiki-based learning, and virtual world and virtual reality. Additionally, the development of mobile devices, games, and virtual worlds continuously promote research attention. Finally, the review showed that scholars and educators are integrating different technologies, such as the mixed use of mobile technology and glosses/annotations for vocabulary learning, and their application into various contexts; one such context being the integration of digital multimodal composing into blended project-based learning.
|Number of pages
|Language Learning and Technology
|Published - Oct 2021
Bibliographical noteWe are grateful for the anonymous reviewers’ helpful comments and suggestions on earlier drafts of this paper. This research was funded by the Teaching Development Grant (102489) from Lingnan University, and the Internal Research Grants (RG15/20-21R, KT16/20-21) from the Education University of Hong Kong.
- Computer Assisted Language Learning
- Structural Topic Modeling
- Mobile Assisted Language Learning