AI 분류 모델을 활용한 초등 AI · 과학 융합 교육프로그램 개발
Development of Elementary AI•Science Convergence Education Program Using An AI Classification Model
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The purpose of this study is to design an artificial intelligence classification model using an AI education platform, and an AI•science convergence program that can be applied to elementary education with the AI classification model. First, an AI classification model was created that has been trained with image data of four representative rock types in the elementary science curriculum. With a recognition rate of 89.1% for the rock samples, it can be fully utilized in the classroom. Based on it, a six-hour-program was developed for elemontary school education. This program was developed based on the 2015 Science Curriculum and the artificial intelligence area of the Standard Model of Next-generation Software Education, and was reviewed by three elementary science education experts and three software education experts to ensure feasibility. The program is easy to be applied to classrooms and will meet the expectations of improving students’ science core competencies and artificial intelligence performance expectation on the Standard Model of Next-generation Software Education.
Abstract
The purpose of this study is to design an artificial intelligence classification model using an AI education platform, and an AI•science convergence program that can be applied to elementary education with the AI classification model. First, an AI classification model was created that has been trained with image data of four representative rock types in the elementary science curriculum. With a recognition rate of 89.1% for the rock samples, it can be fully utilized in the classroom. Based on it, a six-hour-program was developed for elemontary school education. This program was developed based on the 2015 Science Curriculum and the artificial intelligence area of the Standard Model of Next-generation Software Education, and was reviewed by three elementary science education experts and three software education experts to ensure feasibility. The program is easy to be applied to classrooms and will meet the expectations of improving students’ science core competencies and artificial intelligence performance expectation on the Standard Model of Next-generation Software Education.
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