The Global Landscape of Technology-assisted English Language Teaching Research: A Bibliometric Analysis

The purpose of this study is to provide a comprehensive overview of the development and notable patterns in the field of Technology-Assisted English Language Teaching (TAELT) research by conducting a bibliometric analysis of the domain. Author productivity, citation rate, annual scientific production, average citations, most-cited papers, and frequently-used words are only few of the bibliometric markers that can be examined for a more in-depth picture. This research sheds light on the dynamic nature of academic research in the (TAELT) subject. An impressive rise in research output and citations over time is shown by the statistics, which may indicate (TAELT's) growing significance in academic research discourse. Computers and Education, the Proceedings of the ACM International Conference Proceeding Series, and the International Journal of Emerging Technologies in Learning are three of the most important and relevant journals in this area. These publications stand out because of the important role they play in disseminating high-impact research throughout the (TAELT) community. To further illustrate the complexity and multidimensional nature of utilizing technology in English language education, this article examines fundamental problems such e-learning, interactive learning environments, and English language teaching to provide a thorough knowledge of (TAELT) research.

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Publication Details

Journal
Babylonian Journal of Machine Learning
Published
2025-03-25
DOI
https://doi.org/10.58496/bjml/2025/005
Citations
2
Primary Topic
Foreign Language Teaching Methods
Type
article
Field-Weighted Citation Impact
17.85
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article

The Global Landscape of Technology-assisted English Language Teaching Research: A Bibliometric Analysis

Omega John Unogwu, Sara S. Alnakeeb, Eslam Hossam, Ramy Aldallal et al.
2 citations
Babylonian Journal of Machine Learning
Foreign Language Teaching Methods
17.85
article

The Global Landscape of Technology-assisted English Language Teaching Research: A Bibliometric Analysis

Omega John Unogwu, Sara S. Alnakeeb, Eslam Hossam, Ramy Aldallal, Gertrude Milat
article en
2 citations

Abstract

The purpose of this study is to provide a comprehensive overview of the development and notable patterns in the field of Technology-Assisted English Language Teaching (TAELT) research by conducting a bibliometric analysis of the domain. Author productivity, citation rate, annual scientific production, average citations, most-cited papers, and frequently-used words are only few of the bibliometric markers that can be examined for a more in-depth picture. This research sheds light on the dynamic nature of academic research in the (TAELT) subject. An impressive rise in research output and citations over time is shown by the statistics, which may indicate (TAELT's) growing significance in academic research discourse. Computers and Education, the Proceedings of the ACM International Conference Proceeding Series, and the International Journal of Emerging Technologies in Learning are three of the most important and relevant journals in this area. These publications stand out because of the important role they play in disseminating high-impact research throughout the (TAELT) community. To further illustrate the complexity and multidimensional nature of utilizing technology in English language education, this article examines fundamental problems such e-learning, interactive learning environments, and English language teaching to provide a thorough knowledge of (TAELT) research.

Babylonian Journal of Machine LearningVol. 2025
Prince Sattam Bin Abdulaziz University (SA), Al Yarmouk University College (IQ), Universidad Azteca (MX), European Center for Geodynamics and Seismology (LU), University of Business and Technology (SA)
Openalex Percentile: Top 2%
Foreign Language Teaching Methods
17.85
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The Global Landscape of Technology-assisted English Language Teaching Research: A Bibliometric Analysis — Omega John Unogwu, Sara S. Alnakeeb, et al. · Babylonian Journal of Machine Learning (2025) | TGRS Research Map | TGRS