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TF-IDF 분석과 토픽 모델링을 활용한 AI 기반 개별화 학습 국내외 연구동향 분석
An Analysis of Domestic and International Research Trends onAI-based Personalized Learning through TF-IDF and Topic Modeling 원문보기

정보교육학회논문지 = Journal of the Korean Association of Information Education, v.27 no.4, 2023년, pp.453 - 464  

김세영 (서강대학교 교수학습센터)

초록
AI-Helper 아이콘AI-Helper

본 연구는 향후 AI 기반 개별화 학습의 연구 방향을 제안하고자 2016년부터 2023년 4월까지 국내외 연구 동향을 분석하였다. 이를 위해 국내 56개의 KCI 논문, 국외 46개의 SSCI 논문 제목과 초록을 대상으로 TF-IDF 분석과 LDA 기반 토픽 모델링을 실시하였다. 연구결과, 첫째, 국내 연구에 대한 TF-IDF 분석에서 TF는 '학습자', '시스템', '영어', '플랫폼', '개발' 순으로 나타났으며 TF-IDF 기준에서는 '영어', '시스템', '수학', ' 봇', '플랫폼'이 상위권에 위치해 있었다. 둘째, 국내 연구에 대한 LDA 기반 토픽 모델링 결과 5개의 주요 토픽이 도출되었다. 셋째, 국외 연구에 대한 TF-IDF 분석 결과, TF는 'learner', 'system', 'data', 'technology', 'educational' 순으로 나타났으며 TF-IDF 기준에서는 'chatbot', 'collaborative', 'technology', 'gamification', 'system' 이 상위권에 위치해 있었다. 넷째, 국외 연구에 대한 LDA 기반 토픽 모델링 결과 5개의 주요 토픽이 도출되었다.

Abstract ▼ AI-Helper 아이콘AI-Helper

This study analyzed the domestic and international research trends of AI-based personalized learning from 2016 to April 2023 in order to propose research directions for the future fielding of AI-based personalized learning. For this purpose, TF-IDF analysis and LDA-based topic modeling were conducte...

주제어

#AI 기반 개별화 학습   #개별화 학습   #인공지능 활용 교육   #토픽 모델링   #연구동향 분석  

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