Bibliometric Analysis of the Impact of Academic Supervision on Teacher Performance in Educational Research

This study examines the impact of academic supervision on teacher performance in educational research from 2014 to 2023 using bibliometric analysis. This study used the Scopus database and the PRISMA method to search for relevant literature on academic supervision and teacher performance. The analysis used includes WordCloud, word frequency, topic trends, and thematic evolution to analyze journals and conference proceedings. This study highlights the importance of academic supervision in improving teacher performance, with a focus on knowledge development and knowledge transfer. The application of technologies, such as computer vision and convolutional neural networks, can also improve teacher performance. Gender differences in the research approach indicate the need for more in-depth analysis. This research also explores the integration of technology and language analysis, with a focus on network architecture and machine learning. However, this study has limitations, including bibliometric data that does not cover all important aspects and linguistic bias. The results suggest that academic supervision has a significant role in improving teacher performance and modern technology can be an effective tool in this process. This research encourages further development in the use of technology to support academic supervision and teacher performance.