Journal of The Korean Association of Information Education (정보교육학회논문지)

Korea Association of Information Education (한국정보교육학회)
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Designing the Instructional Framework and Cognitive Learning Environment for Artificial Intelligence Education through Computational Thinking

Computational Thinking 기반의 인공지능교육 프레임워크 및 인지적학습환경 설계

  • Shin, Seungki (Computer Science Education, Mary Lou Fulton Teachers College, Arizona State University)
  • 신승기 (애리조나주립대학교 컴퓨터교육전공)
  • Received : 2019.12.09
  • Accepted : 2019.12.20
  • Published : 2019.12.31
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Abstract

The purpose of this study is to design an instructional framework and cognitive learning environment for AI education based on computational thinking in order to ground the theoretical rationale for AI education. Based on the literature review, the learning model is proposed to select the algorithms and problem-solving models through the abstraction process at the stage of data collection and discovery. Meanwhile, the instructional model of AI education through computational thinking is suggested to enhance the problem-solving ability using the AI by performing the processes of problem-solving and prediction based on the stages of automating and evaluating the selected algorithms. By analyzing the research related to the cognitive learning environment for AI education, the instructional framework was composed mainly of abstraction which is the core thinking process of computational thinking through the transition from the stage of the agency to modeling. The instructional framework of AI education and the process of constructing the cognitive learning environment presented in this study are characterized in that they are based on computational thinking, and those are expected to be the basis of further research for the instructional design of AI education.

본 연구에서는 Computational Thinking기반의 인공지능교육을 위한 프레임워크와 인지적 학습환경 구성의 절차를 구현하고자 하였으며, 추후 인공지능교육을 위한 교육과정 설계의 이론적 근거를 제시하고자 하였다. 연구의 결과를 토대로 데이터수집 및 발견의 단계에서 추상화 과정을 통해 알고리즘과 문제해결의 모형을 선택하는 학습모형을 제시하였고 이를 자동화하여 평가하는 단계를 기반으로 문제해결 및 예측하는 과정을 수행함으로써 인공지능을 활용한 문제해결력을 기를 수 있는 Computational Thinking 기반 AI의 교수학습모형을 제시하였다. 인공지능교육에 대한 인지적 학습환경과 관련된 연구를 분석하여 Computational Thinking의 핵심 사고과정 중 하나인 추상화의 단계를 중심으로 절차를 구성하였으며, Agency(학습보조)에서 Modeling(인지적 구조화)으로의 전이를 토대로 학습구성의 단계를 제시하였다. 본 연구에서 제시한 인공지능교육의 프레임워크와 인지적 학습환경 구성의 절차는 Computational Thinking을 기반으로 제시되었다는 점에서 특징을 갖고 있으며 추후 인공지능기반 교수학습연구의 근간이 될 것으로 기대한다.

Keywords

References

  1. Bae, Y. and Shin, S. (2017). The Study and Analysis of the Implications for the Software Education through Case Studies of Applications in the World of Computational Thinking. The Korean Association of Information Education Research Journal, Vol. 9 No. 2, pp. 143-155.
  2. Borowiec, S. (2016). AlphaGo seals 4-1 victory over Go grandmaster Lee Sedol. Retrieved from https://www.theguardian.com/technology/2016/mar/15/googles-alphago-seals-4-1-victory-over-grandmaster-lee-sedol
  3. CAS (2014). Computational Thinking. Retrieved from https://community.computingatschool.org.uk/files/8221/original.pdf
  4. Chartrand, G., Cheng, P. M., Vorontsov, E., Drozdzal, M., Turcotte, S., Pal, C. J., ... & Tang, A. (2017). Deep learning: a primer for radiologists. Radiographics, 37(7), 2113-2131. https://doi.org/10.1148/rg.2017170077
  5. Computer Science Teachers Association (CSTA) and the International Society for Technology in Education (ISTE) (2011). Computational Thinking Teacher Resources. Second Edition. Retrieved from https://c.ymcdn.com/sites/www.csteachers.org/resource/resmgr/472.11CTTeacherResources_2ed.pdf
  6. Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. The International Journal of Information and Learning Technology, 34(2), 133-139. https://doi.org/10.1108/IJILT-09-2016-0048
  7. Goldstine, H. H. (1993). The computer from Pascal to von Neumann. Princeton University Press.
  8. Greenemeier, L. (2017). 20 Years after Deep Blue: How AI Has Advanced Since Conquering Chess. Retreived from https://www.scientificamerican.com/article/20-years-after-deep-blue-how-ai-has-advanced-since-conquering-chess/
  9. Guo, Y. (2017). The 7 Steps of Machine Learning. Retrieved from https://www.slideshare.net/Codemotion/yufeng-guo-coding-the-7-steps-of-machinelearning-codemotion-madrid-2018
  10. Jang, J. (2019). Ministry of Education fosters 5,000 AI teachers. Opened Graduate School of Artificial Intelligence. Retrived from http://www.edupress. kr/news/articleView.html?idxno=4452
  11. Joh, J. (2019). A Study on the Improvement Direction of Education in the 4th Industrial Revolution Period: Focus on Humanity Education and Artificial Intelligence. The Journal of Saramdaum Education Vol. 13, No. 2, pp. 75-89.
  12. Kim, S., Bang, J., and Kwon, H. (2018). A Discussion on the Planning of National Digital Transformation in the Education Sector Vol. 45 No. 4. pp. 173-200.
  13. Krishna Rao, M. R. K. (2005, June). Infusing critical thinking skills into content of AI course. In ACM SIGCSE Bulletin (Vol. 37, No. 3, pp. 173-177). ACM. https://doi.org/10.1145/1151954.1067494
  14. Lison, P. (2015). An introduction to machine learning. Language Technology Group (LTG), 1, 35.
  15. Markoff, J. (2011). Computer Wins on 'Jeopardy!': Trivial, It's Not. Retrieved from https://www.nytimes.com/2011/02/17/science/17jeopardy-watso n.html
  16. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), 12-12.
  17. McCorduck, P., Minsky, M., Selfridge, O. G., & Simon, H. A. (1977, August). History of Artificial Intelligence. In IJCAI (pp. 951-954).
  18. Ministry of Education, Korea (2015). Informatics Curriculum. #2015-74 (Annex 10).
  19. Ministry of Education, Korea (2015). Software Education Instructional Guidance.
  20. Ministry of Education, Korea (November 7, 2019). The plan to foster the AI teachers. [Press release].
  21. Ministry of Science and ICT, Korea (March 20, 2019). 2019 Software Education Leadning School. [Press release].
  22. Oldridge, M. (2017). Is it about coding? No. It's about computational thinking. Retrieved from https://medium.com/@MatthewOldridge/is-itabout-coding-no-its-about-computational-thinking-fe0ba30add61
  23. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.
  24. Park, J. and Shin, N. (2017). Students' perceptions of Artificial Intelligence Technology and Artificial Intelligence Teachers. The Journal of Korean Teacher Education, Vol. 34 No. 2, pp. 169-192. https://doi.org/10.24211/TJKTE.2017.34.2.169
  25. Park, J., Kim, Y., Kim, M., and Lim, K. (2019). Analysis and Development Direction of Education Content onAI speakers Based on Gagne's Instructional Theory: Focused on Amazon Alexa Skills. Journal of Knowledge Information Technology and Systems, Vol. 14 No. 4, pp. 345-358. https://doi.org/10.34163/jkits.2019.14.4.004
  26. Park, P. and Shin, S. (2019), A Study on the Instructional System and Curriculum Design to Evolve the Software Education in Elementary School. Journal of The Korean Association of Information Education Vol. 23, No. 3, pp. 273-282. https://doi.org/10.14352/jkaie.2019.23.3.273
  27. Pranam, A. (2019). Why The Retirement Of Lee Se-Dol, Former 'Go' Champion, Is A Sign Of Things To Come. Retrieved from https://www.forbes.com/sites/aswinpranam/2019/11/29/why-the-retirement-of-lee-se-dol-former-go-champion-is-a-sign-of-things-to-come/#1f7d82173887
  28. Qin, Z. (2019). Beginning of AI and SW Education, Preparing for the Age of AI-Focusing on the development and distribution of AI textbooks in China. Global SW Education Conference.
  29. Quantib (2019). The ultimate guide to AI in radiology. Retrieved from https://www.quantib.com/the-ultimate-guide-to-ai-in-radiology
  30. Ree, S. and Koh, Y. (2017). The Aims of Education in the Era of AI. Journal for History of Mathematics Vol. 30 No. 6. pp. 341-351. https://doi.org/10.14477/jhm.2017.30.6.341
  31. Ryu, M. and Han, S. (2018). The Educational Perception on Artificial Intelligence by Elementary School Teachers. Journal of The Korean Association of Information Education Vol. 22, No. 3, pp. 317-324. https://doi.org/10.14352/jkaie.2018.22.3.317
  32. Shih, W. C. (2019, July). Integrating Computational Thinking into the Process of Learning Artificial Intelligence. In Proceedings of the 2019 3rd International Conference on Education and Multimedia Technology (pp. 364-368). ACM.
  33. Shin, G. (2015). The Big Data World, Principles and Uses. KOCW. Retrieved from http://www.kocw.net/home/cview.do?mty=p&kemId=1132874
  34. Shin, S. and Bae, Y. (2015). A Study on the Hierarchical Instructional System Design of Software Education by School System. Journal of The Korean Association of Information Education Vol. 19, No. 4, pp. 533-544. https://doi.org/10.14352/jkaie.2015.19.4.533
  35. Shin, S., Bae, Y. (2018). The Concept of Computational Thinking through Analysis of Computer Education Framework in the United States and its Implications for the Curriculum of Software Education. Journal of The Korean Association of Information Education. Vol 22, No. 2. pp. 252-262.
  36. Silapachote, P., & Srisuphab, A. (2017). Engineering Courses on Computational Thinking Through Solving Problems in Artificial Intelligence. International Journal of Engineering Pedagogy, 7(3), 34-49. https://doi.org/10.3991/ijep.v7i3.6951
  37. Touretzky, D., Gardner-McCune, C., Martin, F., & Seehorn, D. (2019). Envisioning AI for K-12: What should every child know about AI.
  38. Touretzky, D., Martin, F., Seehorn, D., Breazeal, C., & Posner, T. (2019, February). Special Session: AI for K-12 Guidelines Initiative. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 492-493). ACM.
  39. Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215
  40. Wing, J. (2011). Research notebook: Computational thinking-What and why? The Link Magazine, Spring. Carnegie Mellon University, Pittsburgh. Retrieved from http://link.cs.cmu.edu/article.php?a=600
  41. Zeng, D. (2013). From computational thinking to ai thinking. IEEE Intelligent Systems, (6), 2-4.

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