AI 진단 및 추천 학습맵을 위한 초등학교 과학 오개념 분석
Analysis of Elementary Science Misconceptions for AI Diagnostic and Recommender Learning
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초록
본 논문은 교육부가 추진 중인 AI 디지털교과서의 주요 기능 중 하나인 개인 맞춤 학습을 효과적으로 지원하는 방안을 모색하는 것을 목적으로 한다. 이를 위해 초등학교 과학 과목에서 학생들이 가진 오개념을 분석하고, 이를 지식요소와 연계하여 학습맵을 구축해야 할 필요성을 제시하였다. 초등학교 과학 교과서 및 지도서를 중심으로 분석한 결과 학생들이 ‘운동과 에너지’, ‘물질’, ‘생명’, ‘지구와 우주’ 전 영역에서 고루 오개념을 가지고 있음을 확인하였다. 그러나 현행 교과서 및 지도서에 서술된 오개념에 대한 내용은 제한적이며, 오개념을 지도하는 방법 역시 단편적으로 제시되어 있었다. 따라서 본 연구에서는 학생들의 과학 오개념에 대한 데이터의 수집 및 분석의 필요성, 과학 오개념과 과학 지식 요소 간의 통합, 그리고 오개념 데이터를 반영한 학습맵 구축의 중요성을 강조한다. 본 논문을 통해 향후 도입될 과학 AI 디지털교과서가 학생들이 과학 학습에서 겪는 어려움을 극복하는 데 기역할 수 있기를 기대한다.
Abstract
This paper aims to explore ways to effectively support personalized learning, one of the key features of the AI digital textbooks being promoted by the Ministry of Education. To achieve this, the study highlights the need to analyze students' misconceptions in elementary science and integrate these misconceptions with knowledge elements to build a learning map. Analysis of elementary science textbooks and teacher guides revealed that students hold misconceptions across all areas, including 'Motion and Energy,' 'Matter,' 'Life,' and 'Earth and Space.' However, the current textbooks and guides provide limited content on misconceptions and offer only fragmented guidance on how to address them. Therefore, this study emphasizes the importance of collecting and analyzing data on students' scientific misconceptions, integrating these misconceptions with scientific knowledge elements, and constructing learning maps that reflect misconceptions. It is hoped that the findings of this paper will help the future AI digital textbooks to address the challenges students face in science learning.
참고문헌 (13)
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