온라인 한국어 교육 관련 연구 동향 분석 - 토픽 모델링과 언어 네트워크 분석을 중심으로 -

Abstract

This study aims to investigate the characteristics of knowledge structures and trends in research concerning online Korean language education. The subject of analysis are the journal articles collected by KCI. Topic modeling and semantic network analysis are used to identify the main topics and influential keywords of articles. The analysis yielded the following results. First of all, seven topics were derived through topic modeling: “Development of tools for online Korean language learning”, “Using hybrid online classes”, “Smart Learning and learning using content”, “Management of teachers' online Korean language classes”, “Development of multimedia education materials”, “Media literacy and cultural education” and “Korean language education using new digital technology”. The results of the semantic network analysis showed that keywords with high a degree of centrality throughout the articles included “learning”, “class”, “learner”, “using”, and “development”. In separately analyzing the articles published before and after 2019–when the number of articles increased significantly–it was found that the centrality of “class” was further increased, and that the centrality of “development” was decreased. The results of this study provide implications for understanding trends in online Korean language education research that has evolved reflecting environmental changes and for predicting the direction of future research.