공학교육연구 (Journal of Engineering Education Research)

  • 제28권5호
  • /
  • Pages.40-49
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  • 2025
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  • 1738-6454(pISSN)
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  • 2713-8283(eISSN)
한국공학교육학회 (Korean Society for Engineering Education)
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자연어 기반 콘텐츠 추천 시스템의 효과성 분석에 관한 연구: 초등학생 대상 소프트웨어 교육의 사례를 중심으로

Study on the Effectiveness Analysis of Natural Language-Based Content Recommendation System in Elementary SW Education

  • 김영은 (국립금오공과대학교 컴퓨터공학부 인공지능공학전공 ) ;
  • 권수민 (국립금오공과대학교 컴퓨터공학부 인공지능공학전공) ;
  • 정우제 (국립금오공과대학교 컴퓨터공학부 인공지능공학전공 ) ;
  • 김지형 (국립금오공과대학교 컴퓨터공학부 인공지능공학전공)
  • Kim, Yeongeun (Department of Artificial Intelligence Engineering, Kumoh National Institute of Technology (KIT)) ;
  • Kwon, Sumin (Department of Artificial Intelligence Engineering, Kumoh National Institute of Technology (KIT)) ;
  • Jung, Wooje (Department of Artificial Intelligence Engineering, Kumoh National Institute of Technology (KIT)) ;
  • Kim, Jihyung (Department of Artificial Intelligence Engineering, Kumoh National Institute of Technology (KIT))
  • 투고 : 2025.08.11
  • 심사 : 2025.09.16
  • 발행 : 2025.09.30
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초록

This study aims to evaluate the educational effectiveness of a natural language-based content recommendation system designed for elementary software (SW) education. The proposed system recommends personalized learning content by analyzing students' free-text queries and Entry Test results. It utilizes a dual recommendation mechanism: one based on semantic similarity using a pre-trained KoBERT model, and the other based on meta-learning through a Model-Agnostic Meta-Learning (MAML) algorithm. To assess its impact, a total of 40 elementary school students participated in a 4-session program, during which they interacted with the recommendation chatbot and completed assigned coding tasks. Both pre/post-intervention surveys were conducted to measure changes in learning motivation, engagement, and perceived self-efficacy. The results indicate that students exhibited increased interest and self-directed learning behavior, particularly when recommendations were aligned with their input queries and prior knowledge. The findings support the effectiveness of a natural language-based personalized recommendation approach in fostering engagement and adaptive learning in elementary SW education.

키워드

과제정보

이 논문은 2024년도 네이버 커넥트재단의 지원을 받아 수행된 연구사업이며, 또한 2025년도 경상북도 지역혁신중심대학지원체계(RISE)-(아이디어 창업밸리)의 지원을 받아 수행된 결과입니다.

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