Keyword Network Analysis of Learning Communities Based on Big Data

Objectives The purpose of this study is to identify key keywords related to learning communities using big data and analyze keyword networks to understand major discussions and discourses on learning communities in society. Methods Keyword analysis of learning communities identified ‘learning community,’ ‘teacher,’ ‘professional,’ ‘education,’ and ‘school’ as the most frequent terms. Network analysis showed high connectivity, with distinct characteristics in childcare-centered communities. Sub-network analysis revealed four main clusters: ‘school-centered,’ ‘university student and adolescent-centered,’ ‘early childhood-centered,’ and ‘community-based’ learning communities. Results The keyword frequency analysis of learning communities revealed that ‘learning community,’ ‘teacher,’ ‘professional,’ ‘education,’ and ‘school’ were the most prominent. Network analysis showed a density of 0.935, a diameter of 2, an average degree of 92.58, and an average path length of 1.085. Degree and betweenness centrality aligned with frequently occurring keywords, while closeness centrality highlighted ‘childcare,’ ‘director,’ ‘picture book,’ and ‘edutech,’ indicating unique characteristics of childcare-centered learning communities. Furthermore, the sub-network analysis identified four main clusters: ‘school-centered,’ ‘university student and adolescent-centered,’ ‘early childhood-centered,’ and ‘community-based’ learning communities. Conclusions The results of this study indicate that learning communities are actively utilized across society, contributing to the formation of a collaborative organizational culture and providing lifelong learning opportunities for members of society. Therefore, for learning communities to continue operating and become an integral part of the broader learning culture, it is essential to listen to the voices of diverse participants and support the environmental conditions that enable their voluntary engagement.