포스트 코로나 시대의 국내·외 영어교육 연구동향 분석: 키워드 네트워크 분석 및 토픽 모델링 활용

Analysis of domestic and international trends in English education research in the post-COVID-19 era: Utilizing keyword network analysis and topic modeling

This study investigates post-COVID-19 trends in English education research through keyword network analysis and topic modeling. It analyzes 543 domestic articles from the Korean Citation Index (KCI) and 402 international articles from SPRINGER, published between 2020 and 2024. The research uses tools like NetMiner 4.4.8 for network analysis and Biblio Data Collector for data collection. Word cloud and keyword frequency analyses identify central themes and methodologies. Findings reveal that domestic research focuses on program development, textbook creation, and learner perceptions, while international studies emphasize higher education, technology integration, and policy development. Both domains highlight the importance of digital literacy and online learning. Network analysis identifies core topics and their interrelationships, with centrality measures indicating ‘method’, ‘development’ and ‘practice’ as pivotal. Domestic studies frequently feature ‘program’ and ‘textbook’, while international research often includes ‘university’ and ‘technology’. The study underscores the need for digital literacy education and innovative technologies, providing insights into the evolving landscape of English education research. Additionally, increased frequency of keywords like ‘AI’ and ‘curriculum’indicates growing interest in integrating artificial intelligence and revising curricula to meet new challenges. This comprehensive analysis offers a foundation for enhancing English education practices globally.