| Issue |
E3S Web Conf.
Volume 450, 2023
International Conference on SDGs and Bibliometric Studies (ICoSBi 2023)
|
|
|---|---|---|
| Article Number | 02010 | |
| Number of page(s) | 8 | |
| Section | Engineering and Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202345002010 | |
| Published online | 29 November 2023 | |
Machine learning on academic education: Bibliometric studies
1 Department of Electrical Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia.
2 Department of Economic and Business, Universitas Negeri Surabaya, Surabaya, Indonesia.
3 Department of Data Sains, Universitas Negeri Surabaya, Surabaya, Indonesia.
4 Department of Informatics Engineering, Universitas Negeri Surabaya, Surabaya, Indonesia.
5 Department of Educational Technology, Universitas Negeri Surabaya, Surabaya, Indonesia.
6 Information Technology Department, College of Engineering, Eastern Visayas State University, Philippines.
7 Department of Biology, Universitas Negeri Surabaya, Surabaya, Indonesia.
* Corresponding author: hapsaripeni@unesa.ac.id
The use of Machine Learning exhibits significant promise in facilitating advancements in the field of education. It is vital to conduct a comprehensive review of existing research to ascertain the significance of utilizing Machine Learning as a viable approach to enhance educational advancements. This bibliometric analysis provides a comprehensive overview of the advancements in the application of machine learning techniques within the field of education. This study utilizes publication and citation data from many academic literature sources to elucidate prominent patterns, areas of research emphasis, and scholarly collaborations within this field. The findings of the bibliometric analysis reveal a significant increase in scholarly attention toward the application of machine learning in the field of education during the past several years. The scope of these investigations encompasses a diverse array of subjects, such as personalized learning, predictive analytics, automated evaluation, learning recommendations, and online exam proctoring. The findings of this study also demonstrate a notable rise in the level of collaboration among scholars from many fields, highlighting the significance of interdisciplinary approaches in tackling the intricate challenges associated with the integration of machine learning in education.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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