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디지털 새싹 프로그램의 차별화 학습을 통한 교육 효과 향상에 관한 연구

A Study on Improvement of Educational Effects by Differentiated Learning in Program of ‘Digital Saessak’

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주제분류학제간연구

초록

디지털 새싹 사업은 특별 교육 캠프로서 운영이 되고 있고, 4차산업혁명을 준비하기 위해 학생들에게 로봇, 인공지능, 빅데이터, 소프트웨어를 체험할 수 있는 교육 내용으로 프로그램이 구성되어 있다. 일반적으로 교육 프로그램을 설계하여 프로그램을 구성한다. 이 연구에서는 같은 학력의 교육생이더라도 학생별로 소프트웨어 능력과 수준이 다를 수 있다는 점에 주목하였고, 이를 수준별 학습 프로그램 기반의 구성 기법으로 설계하여 교육을 시행하였을 때 그 효과성을 확인하기 위하여 수행되었다. 구체적으로는 교육생의 소프트웨어 능력을 구분하고, 또한 교육생의 흥미에 따라 교육 주제를 컴퓨터적 사고, 인공지능과 데이터, 문제해결 능력으로 구분하여 수준별 학습 프로그램을 설계하는 방법을 제안한다. 교육의 만족도 분석한 결과 단순한 나열형 교육 프로그램 보다 교육 만족도, 학습의 흥미, 디지털 정보 처리 능력에서 개선된 것으로 평가되었으며, 특히 소프트웨어의 자신감 분야는 8.9%가 향상되는 비교적 우수한 평가도를 보였다.

Abstract

‘Digital Saessak’ program is run as a special education camp and the programs of camps are comprised of educational content to make the students experience robots, artificial intelligence, big data, and software to make them ready for the fourth industrial revolution. Generally, educational programs are configured by considering the periods as short-term, mid-term, and long-term and simultaneously by the educational candidates as students in elementary schools, middle schools, and high schools when the programs are designed initially. This research is based on the idea that the abilities and levels in the software field may vary among the students in the same school and is motivated to show the effectiveness of the education program designed by configuring the scheme based on the differentiated learning program. Specifically, the method in this research is proposed to design the differentiated learning program by considering the software abilities of the education candidates and by configuring the educational subjects such as computational thinking, artificial intelligence, and problem-solving ability. In the view of educational satisfaction the proposed method is evaluated better than the enumerated educational program in the evaluation fields such as educational satisfaction, learning interest, and digital information ability and shows a relatively remarkable evaluation result with 8.9% improvement in the evaluation field of self-confidence in software.

참고문헌 (18)

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