텍스트 마이닝 및 언어 네트워크 분석을 활용한국내 한국어 교육의 CEFR 관련 연구 동향

• 영문명: Trends in CEFR-related research on Korean language education in Korea using text mining and language network analysis
• 발행기관: 한국외국어교육학회
• 저자명: 이윤덕(Yundeok Lee)
• 간행물 정보: 『Foreign Languages Education』Vol.32 No.1, 207~225쪽, 전체 19쪽
• 주제분류: 사회과학 > 교육학
• 파일형태: PDF
• 발행일자: 2025.03.31

국문 초록

This study aims to collect and analyze Common European Framework of Reference for Languages (CEFR)-related research in Korean language education to identify emerging trends. It examines 28 academic articles published in Korea from 2020 to 2024, using text mining and language network analysis methods. Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF) analyses revealed that studies on curriculum design and application in Korean language education appeared with high frequency. Semantic network analysis identified key research directions, such as comparing proficiency level systems in Korean curricula, proposing “mediation” activities based on CEFR, and evaluating CEFR as an assessment tool. Latent Dirichlet Allocation (LDA) topic modeling categorized the studies into three groups: (1) research directly analyzing CEFR, (2) research applying CEFR to overseas Korean language curriculum design, and (3) research comparing existing Korean curricula with CEFR. This study is significant as the first to analyze CEFR-related research trends in Korean language education. By employing objective data analysis tools such as text mining, it enhances the reliability of findings and provides valuable insights into recent research trends.

목차

Ⅰ. 서론

Ⅱ. 연구 방법

Ⅲ. 연구 결과

Ⅳ. 결론 및 제언

참고문헌

키워드

유럽공통참조기준 한국어교육 텍스트마이닝 언어네트워크 Common European Framework of Reference (CEFR) teaching Korean text mining language network