TREN GLOBAL PENELITIAN PENDIDIKAN MATEMATIKA UNTUK ANAK-ANAK: ANALISIS BIBLIOMETRIK PADA DATABASE SCOPUS (1960-2025)
DOI:
https://doi.org/10.23969/jp.v11i01.40979Keywords:
Mathematics Education, Bibliometric Analysis, Research Trends, Children, Scopus DatabaseAbstract
This study aims to map the development of research on mathematics education for children indexed in the Scopus database using a bibliometric analysis approach. Bibliometric methods are employed to evaluate the characteristics of scientific publications based on citation patterns, author productivity, institutional collaboration, and research topic trends in order to identify the direction of research development and dominant themes. The research data were obtained through a structured document selection process using specific keywords with limitations on publication year, social sciences subject area, article document type, and English language, resulting in 546 relevant documents. The analysis was conducted using the R Biblioshiny program to systematically process and visualize bibliographic data through Main Information analysis, publication trend analysis, word cloud, and keyword evolution. The results indicate that research on mathematics education for children has shown a significant and sustained growth from 1960 to 2025, with a sharp increase in publications since 2008 and a peak in 2025. This trend highlights the growing academic attention to mathematics education for children as a strategic field in supporting literacy and numeracy. The word cloud analysis reveals that research themes are dominated by cognitive, pedagogical, and psychological aspects, with a primary focus on mathematics learning, learner characteristics, and learning outcomes. Meanwhile, the keyword evolution analysis shows a shift from foundational studies toward more complex and multidimensional approaches involving affective factors, environmental influences, and pedagogical innovations. Overall, the findings suggest that although research on mathematics education for children has developed comprehensively, there remains significant potential for future research through the integration of deep learning approaches aligned with national education policy directions.
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