초등 생물분류 학습에서 인공지능 융합교육의 적용 사례 연구
A Case Study on Application of Artificial Intelligence Convergence Education in Elementary Biological Classification Learning
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초록
The purpose of this study is to explore the possibility of artificial intelligence convergence education (AICE) in elementary biological classification learning. First, the possibility of AICE was analyzed in the field of 2015 revised elementary life science curriculum. The artificial intelligence biological classification (AIBC) education program targeted plant life. The possibility of AICE in the elementary life science curriculum was suggested through the consultation process of three elementary science education experts. The AIBC education program was developed through the review process of elementary education experts. The results of this study are as follows. First, 8(32%) achievement standards were available for AICE in elementary life science. Second, 18(86%) of the 21 items reviewed by the experts for the AIBC education program developed in this study were positively evaluated. Third, in this study, through the analysis of the possibility of AIBC in the elementary life field and the review of the experts, the AIBC education program including teaching and learning models, strategies, and guidance was developed. The results of this study were based on the review of the experts, and as a follow-up study, applied research to elementary students is needed. It is also hoped that various studies on AICE will be conducted not only in the life field but also in science and other fields. Finally, we expect that the results of this study will be applied to bio-classification learning to help students improve classification capabilities and generate classification knowledge.
Abstract
The purpose of this study is to explore the possibility of artificial intelligence convergence education (AICE) in elementary biological classification learning. First, the possibility of AICE was analyzed in the field of 2015 revised elementary life science curriculum. The artificial intelligence biological classification (AIBC) education program targeted plant life. The possibility of AICE in the elementary life science curriculum was suggested through the consultation process of three elementary science education experts. The AIBC education program was developed through the review process of elementary education experts. The results of this study are as follows. First, 8(32%) achievement standards were available for AICE in elementary life science. Second, 18(86%) of the 21 items reviewed by the experts for the AIBC education program developed in this study were positively evaluated. Third, in this study, through the analysis of the possibility of AIBC in the elementary life field and the review of the experts, the AIBC education program including teaching and learning models, strategies, and guidance was developed. The results of this study were based on the review of the experts, and as a follow-up study, applied research to elementary students is needed. It is also hoped that various studies on AICE will be conducted not only in the life field but also in science and other fields. Finally, we expect that the results of this study will be applied to bio-classification learning to help students improve classification capabilities and generate classification knowledge.
참고문헌 (36)
- Aoun, J. E., "ROBOT-PROOF: Higher education in the age of artificial intelligence" (2017), MIT Press
- 정혜리, "논변활동을 활용한 ‘생물 분류 체계의 변화’ 교육 프로그램 개발", 생물교육, 44(3), pp.463~476 (2016), 한국생물교육학회[DOI]
- 최현동, "초등학교 6학년의 인공자극과 자연자극에 대한 분류 사고", 한국과학교육학회지, 26(1), pp.40~48 (2006), 한국과학교육학회
- Chung, W., "A study on the concept of plant classification among Korean elementary, middle and high school students", Journal of the Korean Association for Science Education, 11(1), pp.25~36 (1991)
- Dewey, J., "Experience and education", the educational forum, 50(3), pp.241~252 (1986), Taylor & Francis Group
- Fry, R., "Experiential learning theory and learning experiences in liberal arts education", New Directions for Experiential Learning, 6, pp.79~ (1979)
- Gabel, D. L., "Introductory science skills" (1993), Waveland Press
- Honey J. N., "The importance of taxonomy in biological education at advanced level", Journal of Biological Education, 20(2), pp.103~111 (1986)
- 허명, "초․중․고등학교 학생들의 식물 분류에 관한 오개념 빈도와 흥미도 연구", 생물교육, 38(1), pp.1~14 (2010), 한국생물교육학회[DOI]
- 조헌국, "머신 러닝을 활용한 이러닝 학습 환경에서의 학습자 성취 예측 모형 탐색", 학습자중심교과교육연구, 18(21), pp.553~572 (2018), 학습자중심교과교육학회[DOI]
- 김은경, "고등학생을 대상으로 한 생물 분류 학습을 위한 실생활 소재 활용 문제중심학습 프로그램 개발", 현장과학교육, 12(1), pp.59~74 (2018), 한국현장과학교육학회[DOI]
- 김희경, "우리나라 과학 교과서에 나타난 기초 탐구 과정 분석: 분류, 예상 및 추리 탐구 요소를 중심으로", 초등과학교육, 26(5), pp.499~508 (2007), 한국초등과학교육학회
- 김갑수, "컴퓨터 프로그램 교육에서 자기조절 학습 모델 개발", 정보교육학회논문지, 19(1), pp.21~30 (2015), 한국정보교육학회[DOI]
- Kim, S., "Analysis of international educational trends and learning tools for artificial intelligence education", The Korean Association of Computer Education, 23(2), pp.25~28 (2019)
- 고연숙, "한국 초등학교 교육과정 변천에 따른 과학 교과서 생명영역 내용분석", 한국과학교육학회지, 36(2), pp.203~219 (2016), 한국과학교육학회[DOI]
- 권용주, "대학생들의 귀납적 탐구에서 나타난 과학적 사고의 유형과 과정", 한국과학교육학회지, 23(3), pp.286~298 (2003), 한국과학교육학회
- Kwon, Y., "Creation and evaluation of science knowledge" (2011), Hakjisa
- Kwon, Y., "Development of the classification ability quotient equation through the analysis of science teachers’ classification knowledge generated in the pollen classification task", Secondary Education Research, 55(3), pp.21~43 (2007)
- 이소영, "초등학생의 학년별 생물분류 개념형성에 대한 연구", 생물교육, 32(1), pp.16~26 (2004), 한국생물교육학회
- 이은주, "탐구 기능의 직접적 수업을 위한 탐구 기능 하위 요소 추출", 한국과학교육학회지, 32(2), pp.236~264 (2012), 한국과학교육학회[DOI]
- 이현주, "고등학교 생물 분류 학습에 효과적인 웹 기반 학습 자료의 구조화에 관한 연구", 생물교육, 30(2), pp.147~157 (2002), 한국생물교육학회
- 이혜원, "초등학생의 과학탐구기능 향상을 위한 명시적이고 반성적인 교수․학습전략 개발 및 적용 -관찰과 분류를 중심으로-", 한국과학교육학회지, 32(1), pp.95~112 (2012), 한국과학교육학회
- 이준호, "중학생들의 생물 분류 개념 이해를 위한 활동중심 학습프로그램 개발", 교사교육연구, 49(2), pp.123~150 (2010), 과학교육연구소[DOI]
- 임채성, "뇌기반 진화적 접근법에 따른 창의적 과학 문제해결 지도 모형 개발", 생물교육, 40(4), pp.429~452 (2012), 한국생물교육학회[DOI]
- Ministry of Education, "Science curriculum", ~ (2015)
- Ministry of Education, "Science 6-2 teacher’s guide", Visang Education, ~ (2019)
- 박기석, "절지동물의 분류를 위한 단계적인 분류 학습 프로그램 개발", 현장과학교육, 5(2), pp.92~108 (2011), 한국현장과학교육학회[DOI]
- Raschka, S., "Python machine learning: Machine learning and deep learning with python, scikit-learn, and TensorFlow 2" (2019), Packt Publishing Ltd
- Rubio, D. M., "Objectifying content validity : Conducting a content validity study in social work research", Social Work Research, 27(2), pp.94~104 (2003)
- Russell, S., "Artificial intelligence. Fears of an AI pionee", Science, 349(6245), pp.252~ (2015)
- 신원섭, "2015개정 초등 5~6학년군 교육과정과 연계한 에너지 동아리 프로그램 개발", 에너지기후변화교육, 9(2), pp.149~159 (2019), 에너지기후변화교육학회[DOI]
- 신원섭, "운동과 에너지에서 인공지능 융합 과학교육의 가능성 탐색 연구", 에너지기후변화교육, 10(1), pp.73~86 (2020), 에너지기후변화교육학회[DOI]
- 신원섭, "초등과학부진학생의 기초과학탐구능력 향상을 위한 중재프로그램 개발 -안구운동을 중심으로-", 한국과학교육학회지, 34(8), pp.795~806 (2014), 한국과학교육학회[DOI]
- 신원섭, "초등과학교육에서 인공지능의 적용방안 연구", 초등과학교육, 39(1), pp.117~132 (2020), 한국초등과학교육학회
- 신원섭, "동화를 활용한 에너지 교육프로그램이 초등학교 6학년 학생들의에너지 소양에 미치는 효과", 에너지기후변화교육, 8(2), pp.207~218 (2018), 에너지기후변화교육학회[DOI]
- 유해미, "대학수학능력시험과 임용고사 분석을 통해 나타난 생물Ⅱ 교과서의 문제점에 관한 연구생태 단원을 중심으로 -", 생물교육, 34(3), pp.307~320 (2006), 한국생물교육학회