인공지능 융합교육을 위한 데이터 기반 교육자료 개발: 감쇠진동을 중심으로
Development of Data-driven Teaching Material for AI Convergence Education: Focused on Damped Oscillation
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초록
인공지능(AI)에 기반한 자동화 시대를 대비하여 과학‧수학‧정보 교육과정의 재구조화와 융합교육의 필요성이 대두되고 있다. 그러나 교사들이 학교 과학 수업에 접목할 수 있는 데이터 기반의 교육자료가 아직은 부족한 실정이다. 이 연구에서는 과학 수업에 접목할 수 있도록 감쇠진동을 중심으로 AI 융합교육을 위한 데이터 기반의 교육자료를 개발하였다. 개발된 교육자료는 최근 기계학습 분야에서 많이 사용되고 있는 딥러닝 지도학습 방법을 이용하였으며, 온라인으로 배포 가능하도록 구글 Colaboratory를 사용하여 작성하였다. 교수학습과정에서 쉽게 사용할 수 있도록 학습 과정과 코드에 대해 자세히 설명하였으며, 직관적인 이해를 돕기 위해 결과를 시각화하였다.
Abstract
In preparation for the era of automation led by Artificial Intelligence(AI), we need for restructuring science‧mathematics‧informatics curriculum and convergence education. However, data-driven teaching materials that teachers can utilize into school science classes are still insufficient. In this study, we have developed data-driven teaching materials for AI convergence education which can be applied to science classes. The teaching materials were developed by Google Colaboratory, which can be easily distributed online, and used the deep supervised learning method that is widely used in machine learning in recent years. Furthermore, we explained the learning process and code in detail for processes for learning and teaching and visualized the results to help users to understand intuitively.
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