Unveiling trends in physics learning with robotics from 2014 to 2024: A bibliometric analysis
Abstract
This study examines 682 articles on physics learning with robotics sourced from scopus, published from 2014 to 2024. The main objective of this study is to uncover the application of robotics in physics learning through bibliometric analysis using R software. This analysis aims to identify, analyze, and present recent trends relevant to the role of robotics in physics learning. The results show that the dominant topics in this field include robotics, deep learning, and reinforcement learning. The number of publications increases significantly, peaking in 2023 with 122 documents, before declining to 112 documents in 2024. The United States is the country with the most authors and contributors with 81 publications, making it a major research center in physics learning with robotics. In addition, the most affiliated institution is the University of California with 48 articles, and the journal “Proceedings - IEEE International Conference on Robotics and Automation” is the most relevant source with 44 publications. Increased international collaboration and cross-disciplinary research is expected to continue to spur innovation in this field. In the future, further research is needed to optimize the use of robotics, especially in improving students' computational ability in physics learning.
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