Owing to the recent advances in information and communications technologies, technology-enhanced language learning (TELL) research has resulted in diverse theories and empirical evidence, showing the complex and dynamic nature of TELL. The retrospective amount of TELL research literature is ever-growing, making it difficult to produce a comprehensive overview. However, knowledge mapping and clustering technologies are capable of visualizing and structuring research literature on a large scale. Therefore, based on 1,295 SCI/SSCI peer-reviewed TELL research articles from 1995 to 2019, we aim to systematically investigate and map the TELL literature to generate implications and insights into future research. Results identify recent major TELL research issues like technology-assisted vocabulary/word learning, computer-facilitated communication, language learning driven by applying computers, virtual reality, mobiles, and digital games, as well as a focus on learners and the use of experimental design. Additionally, scholars are advised to pay attention to issues like robots-assisted language learning, multimodal strategies for language learning, synchronous technologies for language learning, anxiety towards TELL, and artificial intelligence in TELL.
|Title of host publication||Emerging Technologies for Education - 6th International Symposium, SETE 2021, Revised Selected Papers|
|Editors||Weijia JIA, Yong TANG, Raymond S. LEE, Michael HERZOG, Hui ZHANG, Tianyong HAO, Tian WANG|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||10|
|Publication status||Published - 1 Jan 2021|
|Event||6th International Symposium on Emerging Technologies for Education, SETE 2021 - Zhuhai, China|
Duration: 11 Nov 2021 → 12 Nov 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||6th International Symposium on Emerging Technologies for Education, SETE 2021|
|Period||11/11/21 → 12/11/21|
Bibliographical noteFunding Information:
Acknowledgements. This work was supported by General Research Fund (No. 18601118) of Research Grants Council of Hong Kong SAR, China, One-off Special Fund from Central and Faculty Fund in Support of Research from 2019/20 to 2021/22 (MIT02/19-20) of The Education University of Hong Kong, Research Cluster Fund (RG 78/2019-2020R) of The Education University of Hong Kong, and Dean’s Research Fund 2019/20 (IDS-2 2020) of The Education University of Hong Kong.
© 2021, Springer Nature Switzerland AG.
- Bibliometric analysis
- Content analysis
- Knowledge mapping
- Technology-enhanced language learning