[논문]초등과학교육에서 인공지능의 적용방안 연구

신원섭; 신동훈

doi:10.15267/keses.2020.39.1.117

초등과학교육에서 인공지능의 적용방안 연구
A Study on the Application of Artificial Intelligence in Elementary Science Education 원문보기

초등과학교육 = Journal of Korean elementary science education, v.39 no.1, 2020년, pp.117 - 132

Abstract ▼ AI-Helper

The purpose of this study is to investigate elementary school teachers' awareness of Artificial Intelligence (AI) and find out how to apply it in elementary science education. The survey was conducted online and involved 95 teachers working in the metropolitan area. The results of this study are as follows. First, teachers need to learn about the general characteristics of AI and how to apply it to education. Second, science classes had the highest preference for AI among elementary school subjects. Third, the preference for AI application by elementary science field was 68.4% for earth and space, 54.7% for exercise and energy, 32.6% for matter, 27.4% for life. Fourth, AI-based Science Education (AISE) teaching- learning strategies were developed based on AI characteristics and the changing perspective of elementary science education, AISE's teaching-learning strategies are five: 'automation', 'individualization', 'diversification', 'cooperation' and 'creativity' and teachers can use them in teaching design, class practice and evaluation stages. Finally, the creative problem-solving Doing Thinking Making Sharing (DTMS) model was devised to implement the creativity strategy in AISE. This model consists of four-steps teaching courses: Doing, Thinking, Making and Sharing based on the empirical learning theory. In the future, follow-up research is needed to verify the effectiveness of this model by applying it to elementary science education.

주제어

표/그림 (19)

표 Table 1. Participants
표 Table 2. Questionnaire
표 Table 3. Elementary school teachers' perception of AI (N=95)
그림 Fig. 1. Role of AI teacher.
그림 Fig. 2. AI education direction.
표 Table 4. Elementary school teachers' awareness of AI education (N=95)
그림 Fig. 3. Key elements of AI education.
그림 Fig. 4. AI education application period.
그림 Fig. 5. AI education course preference.
그림 Fig. 6. AI teaching experience.
그림 Fig. 7. AI suitability by science area.
그림 Fig. 8. Teaching-learning model.
표 Table 5. Preference of AI application by each units of elementary science
그림 Fig. 9. Teaching-learning method.
그림 Fig. 10. AI preference by evaluation area.
그림 Fig. 11. AI preference by evaluation method.
그림 Fig. 12. AI features.
그림 Fig. 13. AISE's strategy.
그림 Fig. 14. CPS DTMS model.

참고문헌 (40)

An, B. J. (2018). [Myeongkyung Honorary Reporter] Edutech Big Bang AI teacher comes out within five years. Maeil Economy 7, Retried October 4, 2018, from https://www.mk.co.kr/news/business/view/2018/10/616768/
Aoun, J. E. (2017). ROBOT-PROOF: Higher education in the age of artificial intelligence. Cambridge, MA: MIT Press.
Baek, C. H., Choi, J. H. & Lim, S. W. (2018). Review and suggestion of characteristics and quality measurement items of artificial intelligence service. Journal of the Korean Society for Quality Management, 46(3), 677-694.
Cha, Y. R. (2018). Artificial Intelligence (AI) strategy in the advertising and media industry: In-Depth interview. The Journal of the Korea Contents Association, 18(9), 102-115.
Choi, M, Y. & Lee, T. W. (2019). Predicting the state of AI education and changing roles of schools and teachers. Paper Presented at the Korean Society for Computer Education Conference, 23(2), 85-88.
Choi, S. & Kim, H. B. (2019). Investigate the application of biology class reflecting the characteristics of virtual reality. Biology Education, 47(3), 263-277.

상세보기
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
Dewey, J. (1986). Experience and education. In The educational forum (Vol. 50, No. 3, pp. 241-252). Taylor & Francis Group.
Follows, J. (2019). The test of China's future [How AI is made in China]. Translator: Lee Woo-hyun, Seoul: Serene.
Fry, R. & Kolb, D. (1979). Experiential learning theory and learning experiences in liberal arts education. New Directions for Experiential Learning, 6, 79.
Kakao Foreign Policy Team (2018). KAKAO AI REPORT. Seoul: Book by Book.
KIBS Editorial Department (2019). "Fostering AI convergence talent with the easiest AI education in the world": [Interview] Prof. Chang Mook Kang, Dean of the department of AI convergence, Global Cyber University. Brain, 78, 34-37.
Kim, C. (1996). A study of the development of procedures for selecting science-teaching-learning models that are appropriate for class content or activities. Cheongju National University of Science Research Institute, 17, 143-170.
Kim, J. H. (2016). Fourth industrial revolution, education in the age of artificial intelligence. STSS Conference on Sustainable Science, 21-29.
Kim, K. (2015). A self-regulated learning model development in computer programming education. Journal of The Korean Association of Information Education, 19(1), 21-30.

원문보기 상세보기
Kim, K. & Park, Y. (2017). A development and application of the teaching and learning model of artificial intelligence education for elementary students. Journal of The Korean Association of Information Education, 21(1), 139-149.
KOSAF (2019). 2019 AI convergence education conference. 2019 AI Convergence Education Conference Policy Kit.
Kwon, Y. J., Jung, J. S., Shin, D. H., Lee J. K., Lee I. S. & Byun J. H. (2011). Generation and evaluation of scientific knowledge. Seoul: Governor.
Lee, J. H. & Huh, N. (2018). Exploring the relationship between changes in mathematics education and artificial intelligence. E-Mathematics Education Proceedings, 32(1), 23-36.
Lee, S. H. & Kim, Y. S. (2018). Artificial intelligence appeared in school. Korean Information Science Society, 36(11), 44-50.
Lim, C. S. (2012). Development of an instructional model for brain-based evolutionary approach to creative problem solving in science. Biology Education, 40(4), 429-452.

상세보기
Lim, C. S. (2014). Develop and apply scientific creativity assessment formulas. Elementary Science Education, 33(2), 242-257.

원문보기 상세보기
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Laak, J. A. V. D., Ginneken, B. V. & Sanchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical Image Analysis, 42, 60-88.

상세보기
Maderer, J. (2016). Artificial intelligence course creates AI teaching assistant. Georgia Tech News Center, 9. Retried May 9, 2016, from http://www.news.gatech.edu/2016/05/09/artificial-intelligence-course-creates-ai-teaching-assistant.
Ministry of Education (2015). Practical arts (Technology & Home)/information curriculum. Ministry of Education.
Ministry of Education (2019). Science 6-2 teacher's guide. Visang Education.
Minsky, M.(1968). Semantic information processing. Cambridge, MA: MIT Press,
Park, H., Bae, S., & Park, J. (2014). The likert scale attention points applied to research on attitude and interests on science education. Journal of the Korean Association for Science Education, 34(4), 385-391.

원문보기 상세보기
Park, J. H. & Shin, N. M. (2017). Analysis of awareness of artificial intelligence technology and artificial intelligence teachers: From the elementary, middle, and high School students' perspective. Korean Teacher Education Research, 34(2), 169-192.
Russell, S. & Bohannon, J. (2015). Artificial intelligence. Fears of an AI pioneer. Science, 349(6245), 252.

상세보기
Ryu, M. Y. & Han, S. G. (2018). Educational perceptions of artificial intelligence in elementary school teachers. Journal of Information and Education, 22(3), 317-324.
Shin, D. G. (2019). Explore how to use AI chatbots to improve your English writing skills. Teacher Education, 35(1), 41-55.
Shin, H. W. (2018). ‘Characteristics' and ‘Goals' of foreign languages in the age of artificial intelligence. Foreign Language Education, 25(4), 133-157.

상세보기
Shin, S. I., Ha, M. S. & Lee, J. K. (2018). Exploring elementary school students' image on artificial intelligence. Elementary Science Education, 37(2), 126-146.
Shin, W. (2016). The effect of the elementary science inquiry classes on pre-service elementary teachers' attitude toward science, perception of science teaching-learning and personal science teaching efficacy. Biology Education, 44(3), 555-568.
Shin, W. & Shin, D. (2014). The development of intervention program for enhancing elementary science-poor students' basic science process skills. - Focus on eye movement analysis -. Journal of the Korean Association for Science Education, 34(8), 795-806.

원문보기 상세보기
Sohn, Y. H. (2016). Legal issues in the age of artificial intelligence. Law and Policy Studies, 16(4), 305-329.
Song, J. W., Kang, S. J., Kwak, Y. S., Kim, D. G., Kim, S. H., Na, J. Y., Do, J. H., Min, B. G., Park, S. C., Bae, S. M., Son, Y., Son, J. W., Oh, P. S., Lee, J. K., Lee, H. J., Lim, H., Jung, D. H., Jung, Y. J., Jung, J. H. & Kim, J. H. (2019). Future generation science education standard. Korea Creative Foundation.
Wilson, H. J. & Daugherty, P. R. (2018). Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review, 96(4), 114-123.

상세보기
Yoon, S. G. (2018). Social studies education in the age of artificial intelligence: Relationship with artificial intelligence. Social Studies Education Research, 25(2), 1-20.

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