정보교육학회논문지 (Journal of The Korean Association of Information Education)

  • 제24권6호
  • /
  • Pages.573-584
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  • 2020
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  • 1229-3245(pISSN)
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  • 2733-8428(eISSN)
한국정보교육학회 (Korea Association of Information Education)
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DOI QR Code

인지 모델링기반 인공지능 교육 프로그램을 적용한 초등학생의 인공지능 이미지 변화 분석

Analysis of changes in artificial intelligence image of elementary school students applying cognitive modeling-based artificial intelligence education program

  • 김태령 (서울세검정초등학교) ;
  • 한선관 (경인교육대학교 컴퓨교육과)
  • Kim, Tae-ryeong (Seoul Segumjung Elementary School) ;
  • Han, Sun-gwan (Dept of Computer Education, GyeongIn National University of Education)
  • 투고 : 2020.09.24
  • 심사 : 2020.11.26
  • 발행 : 2020.12.31
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초록

본 연구는 초등학생들의 인공지능에 대한 이미지를 긍정적으로 향상시키고자 하는 인지 모델링기반 인공지능 알고리즘 교육 프로그램의 개발에 관한 것이다. 먼저 인공지능 알고리즘 중 협력필터링의 개념을 분석하고 이를 인지모델링 방법을 활용하여 교육 프로그램을 개발하였다. 이후 전문가 타당도 검사를 통해 인지 모델링기반의 콘텐츠 개발 방법과 개발된 프로그램에 대한 적절성이 CVR .80 이상으로 타당함을 확인하였다. 개발 프로그램은 초등학교 6학년 학생들에게 수업으로 적용하였고 형용사 단어 23쌍을 이용한 의미분별법을 이용하여 사전-사후에 인공지능에 대한 학생들의 이미지 인식의 변화를 살펴보았다. 학생들의 인공지능에 대한 이미지는 총 23개 단어 쌍 중 12개에서 유의미한 긍정적 변화를 확인할 수 있었다.

This study is about the development of AI algorithm education program using cognition modeling to positively improve students' image on AI. First, we analyzed the concept of user-based collaborative filtering and developed the education program using the cognition modeling method. We checked the adequacy of program through the expert validity test. Both CVR values for the content development method of cognitive modeling and the developed program showed validity above .80. We applied the developed program to elementary school students in class. The test was conducted using a semantic discrimination to examine changes in students' perception of artificial intelligence before and after. We were able to confirm that the students' AI images were significant positive change in 12 of the 23 words in the adjective pair.

키워드

참고문헌

  1. Bandura, A. (2008). Observational learning. In W. Donsbach, (Ed.) International encyclopedia of communication. Oxford, UK: Blackwell.
  2. CBSE (2019). Artificial intelligence curriculum. from World Wide Web: http://www.cbseacademic.nic.in/web_material/Curriculum20/AI_Curriculum_Handbook.pdf. Retrieved July 1, 2020.
  3. Chamizo, J. A. (2013). A new definition of models and modeling in chemistry's teaching. Science & Education, 22(7), 1613-1632. https://doi.org/10.1007/s11191-011-9407-7
  4. Datameetsmedia. (2017). an overview of recommendation systems from World Wide Web: http://datameetsmedia.com/an-overview-of-recommendation-systems/ retrieved November 10, 2020.
  5. Gomes, A. & Mendes, A. J. (2007). An environment to improve programming education. In Proceedings of the 2007 international conference on Computer systems and technologies, 1-6.
  6. Hwang Hyunmi, Bang Jeongsuk (2007). A Survey on the Comprehension of Graphs of Sixth Graders. School Mathematics, 9(1), 45-64.
  7. Jeon Woocheon (2017). Analysis of the current state of artificial intelligence education in each country. Review of Korean Society for Internet Information, 18(1), 13-18.
  8. Kim Gapsoo, Park Youngki (2017). A Development and Application of the teaching and learning model of Artificial Intelligence Education for Elementary Student. Journal of the Korean Association of Information Education, 21(1), 137-147. https://doi.org/10.14352/jkaie.21.1.137
  9. Kim Gapsoo (2019). An Artificial Intelligence Education Program Development and Application for Elementary Teachers. Journal of the Society for Information Education, 23(6), 629-637.
  10. Kim Hanje, Jeong Yongjae, Jang Myungdeok (2013). Elementary Pre-service Teachers' Conceptions on 'the Freezing Point Depression' and a Proposal of Explanatory Models. Journal of Korean elementary science education, 32(2), 206-224. https://doi.org/10.15267/KESES.2013.32.2.206
  11. Kim Soohwan, Kim Seonghoon, Kim Hyuncheol (2019). Analysis of International Educational Trends and Learning Tools for Artificial Intelligence Education. Journal of the Korean Society for Computer Education, 23(2), 25-28.
  12. Lee Sooyeon, Kim Myunghye, Kim Sunnam, Park Kyunghee (2003). A study on female students' computer and internet use and attitude. The Women's Studies, 64, 79-108.
  13. Lee Sunjin (2018). Development and application of artificial intelligence education program for elementary school students based on recommendation system. Jeju National University Graduate School of Education. Master's Thesis.
  14. Lee Youngho (2019). An Analysis of the Influence of Block-type Programming Language-Based Artificial Intelligence Education on the Learner's Attitude in Artificial Intelligence. Journal of the Society for Information Education, 23(2), 189-196.
  15. Nilashi, M., Bagherifard, K., Ibrahim, Alizadeh, H., Lasisi, A., & Roozegar, N. (2013). Collaborative Filtering Recommender Systems. Research Journal of Applied Sciences, Engineering and Technology. 5.
  16. Ministry of Education (2020). 2020 Ministry of Education work plan. from World Wide Web: https://www.moe.go.kr/boardCnts/fileDown.do?m=020402&s=moe&fileSeq=162a0febc37f228e44507d1a96e3d22d retrieved July 5, 2020.
  17. Ministry of Science and ICT (2020). 2020 Ministry of Science and ICT work plan. from World Wide Web : https://www.msit.go.kr/web/msipContents/contentsView.do?cateId=_policycom2&artId=2505054. retrieved July 7, 2020
  18. NGSS Lead States (2013). Next generation science standards: For states, by states. Washington, DC: National Academies Press.
  19. Oh Gyeongsun, An Seongjin (2015). A study on the relationship between difficulty in learning to program and Computational Thinking. The Journal of Korean Association of Computer Education, 18(5), 55-62. https://doi.org/10.32431/KACE.2015.18.5.006
  20. Park Jungho (2020). The Case Study on Artificial Intelligence Based Maker Education for Pre-Service Teacher. Journal of Digital Contents Society, 21(4), 701-709. https://doi.org/10.9728/dcs.2020.21.4.701
  21. Ryu Miyoung, Han Sungwan (2017). Image Analysis of Artificial Intelligence Recognized by Elementary School Students. Journal of the Korean Association of Information Education, 21(5), 527-535. https://doi.org/10.14352/jkaie.21.5.527
  22. Ryu Miyoung, Han Sungwan (2018). AI Education Programs for Deep-Learning Concepts. Journal of the Korean Association of Information Education, 23(6), 583-590.
  23. Liu, S. (2019). Artificial intelligence software market revenue worldwide 2018-2025. Tractica.
  24. Seo Bongwon (2016). Content recommendation algorithm evolution. Broadcasting & Trend Insight 5. Korea Creative Content Agency. from World Wide Web: http://www.kocca.kr/insight/vol05/vol05_04.pdf. retrieved August 27, 2020.
  25. Shin Eunju, Kwon Ohnam (2001). A Study of Exploration-Oriented Mathematical Modeling. The Journal of Educational Research in Mathematics, 11(1), 157-177.
  26. Shin Wonseop (2020). A case study on the application of artificial intelligence convergence education in elementary biology classification learning. Elementary Science Education, 39(2), 284-295.
  27. Son, L. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87-104. https://doi.org/10.1016/j.is.2014.10.001
  28. Spice, B. (2018). Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence. from World Wide Web: https://www.cmu.edu/news/stories/archives/2018/may/ai-undergraduate-degee.html. Retrieved June 27, 2020.
  29. Stipek, D. J. (1988). Motivation to learn: From theory to practice. Englewood Cliffs, NJ: Prentice-Hall.
  30. Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What Should Every Child Know about AI?. In Proceedings of the AAAI Conference on Artificial Intelligence, 33, pp. 9795-9799.
  31. Yoon Jinyoung, Kim Yoomi, Jae Jihwan, Kim Yeonhyung (2019). A Study on the Media Art STEAM Education Program Using Data Science and Artificial Intelligence. The Korean Society of Science & Art, 37(5), 265-276. https://doi.org/10.17548/ksaf.2019.12.30.265
  32. Zimmerman, B. J., Bandura, A., & Martinez-Pons, M. (1992). Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting. American educational research journal, 29(3), 663-676. https://doi.org/10.3102/00028312029003663