Andi Muh. Akbar Saputra, A. Hermila, Ashabul Taufik, Indra Farman
This study aims to map the trends and development of research on virtual laboratories at the secondary school level in Indonesia over the past ten years. The method used in this study is a combination of bibliometrics, text mining, and machine learning algorithms in the form of K-means clustering. Data were taken from four main databases, namely Scopus, Semantic Scholar, Google Scholar, and Crossref, with a total of 1522 articles analyzed. The analysis process includes data extraction of publication year, author, and abstract, which are then processed to identify dominant keywords and thematic clustering patterns based on content similarity. The results showed that articles related to virtual laboratories had increased significantly in the last five years, with Google Scholar and Crossref being the largest contributors in terms of the number of publications. Word clouds from each database revealed a major focus on science learning, educational technology, and teacher-student interaction. K-Means clustering analysis showed that articles could be classified into three to four clusters, representing different levels of influence, ranging from popular articles with high citations to contextual articles with limited influence. The findings show that despite the growing adoption of virtual laboratories, there is a gap between innovation and academic recognition. This study contributes to building a data-driven literature map of virtual laboratory research in Indonesian secondary schools and provides a basis for decision-making for future research planning, education policy, and strengthening scientific publications. © 2025 IEEE.
Universitas Islam Makassar, Information Technology Education, Makassar, Indonesia; Universitas Negeri Gorontalo, Department of Informatics Engineering, Gorontalo, Indonesia
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