텍스트 마이닝을 활용한 영어 말하기 교수 및 학습의 연구 동향 분석: 매트릭스 분석과 토픽모델링의 활용

Abstract

To understand trend analysis of research topics and topic-related keywords in English speaking education, this study conducted text mining of big data academic materials. The keywords were "English" and “Speaking,” and 2622 paper totals were analyzed. In order to collect research trend data for this study, a search of KCI was performed using the AND function of the keywords “English” and “speaking”. For the analysis, textome was used for text mining, and frequency and relevance of major keywords, and network topic modeling were performed. The results provide insight into possible future research directions by analyzing research trends in English speaking education. Three broad research areas were identified, which included: a focus on subjects, studies on curriculum and tools, and an investigation of correlations between English ability and other areas designed to improve speaking ability. Topic modeling analysis extracted ten research topics. Topic-related keywords that most frequently appeared were general words such as influence, Korean, use, and research. In addition, learner-centered research was a strong focus, with an interest in researching ways to improve learners' speaking ability by examining the differences in speaking ability and components of speaking performance according to the types of learners and educational institutions.