과학교육연구지 (Journal of Science Education)

  • 제44권1호
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  • Pages.92-111
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  • 2020
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  • 1225-3944(pISSN)
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  • 2733-4074(eISSN)
경북대학교 과학교육연구소 (Science Education Research Institute Kyungpook National University)
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과학적 문제해결과정과 컴퓨팅 사고의 관련성 탐색을 통한 컴퓨팅 사고 기반 과학 탐구(CT-SI) 모형의 제안

Suggestion of Computational Thinking-Scientific Inquiry (CT-SI) Model through the Exploration of the Relationship Between Scientific Problem Solving Process and Computational Thinking

  • 투고 : 2020.02.21
  • 심사 : 2020.04.24
  • 발행 : 2020.04.30
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초록

컴퓨팅 사고(computational thinking)는 2015 개정 과학교육과정 및 미국의 차세대 과학교육표준(NGSS)에서 새로운 탐구 기능 혹은 역량으로 제시되고 있다. 특히, 2014년부터 교육부가 소프트웨어 교육을 필수화함에 따라 컴퓨팅 사고에 관한 관심은 더욱 커지고 있다. 그러나 과학교육 분야에서 컴퓨팅 사고를 어떻게 접목할 것인가에 대한 논의는 아직 부족한 실정이다. 이에, 본 연구에서는 다양한 관련 분야의 문헌 분석을 통해 컴퓨팅 사고 요소들을 과학 탐구에 접목하는 방안을 마련하고자 하였다. 이를 위해 먼저 컴퓨팅 사고의 요소에 대한 여러 정의를 정리하였고, 이를 활용한 모형을 개발하기 위해 일반 문제해결 과정과 과학적 탐구과정들을 종합적으로 분석하였다. 마지막으로 컴퓨터 과학 분야에서 문제해결에 접목한 사례들과 비교하여 컴퓨팅 사고 기반 과학 탐구(CT-SI) 모형의 요소들을 정리하였다. 정리된 요소들을 이학 전문가들에게 제공하여 각 분야의 연구 과정과 컴퓨팅 사고 요소들을 접목하여 설명하게 한 후, 이를 기반으로 문제발견형 CT-SI 모형과 문제해결형 CT-SI 모형을 개발하였다. 개발된 두 모형은 이학 전문가들에 의해 모형의 단계가 각 분야의 연구에 활용 가능하다고 검토받았으며, '문제발견형'은 과학 연구 과정에서 정보를 선별하고 문제를 분석하는 과정과 이론적 연구에서 근거를 기반으로 하는 추론 연구 과정에 적합하다고 응답하였다. '문제해결형'은 과학의 일반적인 연구 과정 및 공학설계를 활용한 공학적 문제해결과정에 적합하다고 응답하였다. 또한, 현장 교사 2인에 의해 중고등학교 현장 탐구 수업에 적용 가능함을 확인하였다. 본 연구에서 개발된 모형은 다양한 과학 교과의 탐구 활동과 연계할 수 있으며 이를 통해 2015 개정 교육과정에서 제시하고 있는 '자료의 수집, 분석 및 해석', ' 수학적 사고와 컴퓨터 활용' 역량을 길러줄 수 있을 것이다.

The 2015 revised science curriculum and NGSS (Next Generation Science Standard) suggest computational thinking as an inquiry skill or competency. Particularly, concern in computational thinking has increased since the Ministry of Education has required software education since 2014. However, there is still insufficient discussion on how to integrate computational thinking in science education. Therefore, this study aims to prepare a way to integrate computational thinking elements into scientific inquiry by analyzing the related literature. In order to achieve this goal, we summarized various definitions of the elements of computational thinking and analyzed general problem solving process and scientific inquiry process to develop and suggest the model. We also considered integrated problem solving cases from the computer science field and summarized the elements of the Computational Thinking-Scientific Inquiry (CT-SI) model. We asked scientists to explain their research process based on the elements. Based on these explanations from the scientists, we developed 'Problem-finding' CT-SI model and 'Problem solving' CT-SI model. These two models were reviewed by scientists. 'Problem-finding' model is relevant for selecting information and analyzing problems in the theoretical research. 'Problem solving' is suitable for engineering problem solving process using a general research process and engineering design. In addition, two teachers evaluated whether these models could be used in the secondary school curriculum. The models we developed in this study linked with the scientific inquiry and this will help enhance the practices of 'collecting, analyzing and interpreting data,' 'use of mathematical thinking and computer' suggested in the 2015 revised curriculum.

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참고문헌

  1. Arden, G., & Vaegan (1983). Electroretinograms evoked in man by local uniform or patterned stimulation. Journal of physiology, 341(1), 85-104. https://doi.org/10.1113/jphysiol.1983.sp014794
  2. Bransford, J. D., & Stein, B. S. (1984). The IDEAL problem solver. New York, NY: Freeman.
  3. Brennan, K., & Resnick, M. (2012). New framework for studying and assessing the development of computational thinking. Proceeding of the 2012 American Educational Research Association Meeting, Vancouver, BC, Canada. Retrieved from https://web.media.mit.edu/-kbrennan/files/Brennan_Resnick_AERA2012_CT.pdf
  4. Brinda, T., Puhlmann, H., & Schulte, C. (2009). Bridging ICT and CS - Educational standards for computer science in lower secondary education. Proceedings of the 14th annual ACM SIGCSE conference on innovation and technology in computer science education, 288-292. New York, NY: ACM.
  5. Burke, K., Poock, J., Greenbowe, T., & Hand, B. (2005). Training chemistry teaching assistants to use the science writing heuristic. Journal of College Science Teaching, 35(1), 36-41.
  6. Cho, Y., Seong, J., & Lee, H. (2008). Creativity education: developing creative problemsolving abilities. Seoul: Ewha Womans University Press.
  7. Choi, H. (2014). Developing lessons and rubrics to promote computational thinking. Journal of the Korean Association of Information Education, 18(1), 57-64. https://doi.org/10.14352/jkaie.2014.18.1.57
  8. Choi, H. (2018). Domestic literature review on computational thinking development through software programming education. The Korean Society for Educational Technology, 34(3), 743-774.
  9. Choi, H., & Kim, M. (2017). Designing a new teacher education course for integrating design thinking with computational thinking. Journal of the Korean Association of Information Education, 21(3), 343-350. https://doi.org/10.14352/jkaie.21.3.343
  10. Choi, S. (2016). A study on teaching-learning for enhancing computational thinking skill in terms of problem solving. The Korean Association of Computer Education, 19(1), 53-62.
  11. Dewey, J. (1910). How we think. Boston, MA : D. C. Heath & Co.
  12. Feldhusen, J. F., & Treffinger, D. J. (1977). The role of instructional material in teaching creative thinking. Gifted Child Quarterly, 21(4), 450-459. https://doi.org/10.1177/001698627702100405
  13. Foster, I. (2006). 2020 Computing: a two-way street to science's future. Nature, 440, 419. https://doi.org/10.1038/440419a
  14. Han, S., & Kim, H. (2019). A study on the change of the perception of students' computational thinking and scientific attitudes in earth science classes using a block-based coding. Journal of the Korean Society of Earth Science Education, 12(2), 131-140.
  15. Harwood, W. S. (2004). A new model for inquiry. Journal of college science teaching, 33(7), 29-33.
  16. Heo, M. (1984). Development of evaluation table for scientific exploration. Journal of the Korean Association for Science Education, 4(1), 57-64.
  17. Hwang, Y. (2015). Development of strategies for education about research design to student researchers in science and engineering through extraction of problem specification elements (Doctoral dissertation). Daegu: Kyoungpook National University.
  18. Hwang, Y., Mun, K., & Park, Y. (2016). Study of perception on programming and computational thinking and attitude toward science learning of high school students through software inquiry activity: Focus on using scratch and physical computing materials. Journal of the Korean Association for Science Education, 36(2), 325-335. https://doi.org/10.14697/jkase.2016.36.2.0325
  19. International Society for Technology in Education [ISTE] & Computer Science Teachers Association [CSTA]. (2011). Computational thinking in K-12 education teacher resource, 2nd Ed. Retrieved from http://www.iste.org/docs/ct-documents/ctteacher-resources_2ed-pdf.pdf?sfvrsn=2
  20. International Society for Technology in Education [ISTE] & Computer Science Teachers Association [CSTA]. (2011). Computational thinking leadership toolkit 1st edition. Retrieved from http://csta.acm.org/Curriculum/sub/CurrFiles/471.11CTLeadershiptToolkit-SP-vF.pdf
  21. Jeon, Y., & Kim, S. (2017). 2015 Revised curriculum and computational thinking. 2017 software education leader teacher training course. Daegu : KERIS.
  22. Kalelịoğl, F., Gülbahar, Y., & Kukul, V. (2016). A framework for computational thinking based on a systematic research review. Baltic Journal of Modern Computing, 4(3), 583-596.
  23. Kim, H., & Choi, S. (2019). The effects of instructional strategies using the process of procedural thinking on computational thinking and creative problem-solving ability in elementary science classes. Journal of Science Education, 43(3), 329-341. https://doi.org/10.21796/jse.2019.43.3.329
  24. Kim, M., & Kim, S. (2018). Korean secondary students' computational thinking based on problem solving. Korean Association for Learner-centered Curriculum and Instruction, 18(16), 807-830. https://doi.org/10.22251/jlcci.2018.18.16.807
  25. Kim, S. (2015). Development of scratch code analysis system for assessment about concepts of computational thinking. The Journal of Korean Association of Computer Education 18(6), 13-22. https://doi.org/10.32431/KACE.2015.18.6.002
  26. Kim, S., & Kim, T. (2018). The effect of data literacy on students' learning motivation and academic achievement in secondary problem solving class based on computational thinking. The Korean Society for the Study of Teacher Education, 34(4), 125-144. https://doi.org/10.14333/KJTE.2018.34.4.125
  27. Kim, Y. (2013). Thinking ability education. Seoul: Yuwon Books.
  28. Lee, A. (2019). Domestic research trend analysis of computing thinking. The Korea Contents Association, 19(8), 214-223.
  29. Lee, C. (2017). Effects of computational thinking based real life problem solving learning on elementary school student's computational thinking. The Society of Korean Practical Arts Education Research, 23(4), 91-107. https://doi.org/10.29113/skpaer.2017.23.4.091
  30. Lee, J., Kim, J., & Kim, J. (2018). Effects of the experience in developing physics teaching materials based on computational thinking for improvement of science teachers' and pre-service teachers' technological pedagogical and content knowledge. New Physics: Sae Mulli, 68(2), 202-216. https://doi.org/10.3938/NPSM.68.202
  31. Lee, K. (2019). Computational thinking education teaching method research for non-major subjects. The Korean Association of General Education, 13(1), 321-343.
  32. Liu, J., & Wang, L. (2010). Computational thinking in discrete mathematics. Second International Workshop on Education Technology and Computer Science (ETCS), 1, 413-416.
  33. Mansfield, R. S., & Busse, T. V. (1981). The psychology of creativity and discovery: Scientists and their work. Chicago, IL: Nelson-Hall.
  34. Marshall, J. A., & Carrejo, D. J. (2008). Students' mathematical modeling of motion. Journal of Research in Science Teaching, 45(2), 153-173. https://doi.org/10.1002/tea.20210
  35. Ministry of Education [MOE]. (1997). Science curriculum. Seoul: Ministry of Education: 1997-15(9)
  36. Ministry of Education [MOE]. (2015a). Guide of operating SW education. Sejong: Ministry of Education.
  37. Ministry of Education [MOE]. (2015b). The national curriculum for the primary and secondary schools. Sejong: Ministry of Education.
  38. Ministry of Education [MOE]. (2015c). Science curriculum. Sejong: Ministry of Education.
  39. National Research Council [NRC]. (2000). Inquiry and the national science education standards. Washington DC: National Academy Press.
  40. National Research Council [NRC]. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, D.C.: The National Academies Press.
  41. National Research Council [NRC]. (2012). A framework for K-12 science education: Practices, crosscutting concepts, and core ideas. Washington, DC: The National Academies Press.
  42. Oh, K., & Ahn, S. (2013). A study on the problem-solving process model based on the computational thinking to improve creativity. The Korean Association of Computer Education Winter Conference Paper, 17(1), 183-186.
  43. Park, S., & Ahn, S. (2016). An exploratory study of the competence of computational thinking: for software developer. The Journal of Korean Association of Computer Education, 19(5), 41-53. https://doi.org/10.32431/KACE.2016.19.5.004
  44. Park, Y., & Green, J. (2019). Bringing computational thinking into science education. Journal of the Korean Earth Science Society, 40(4), 340-352. https://doi.org/10.5467/JKESS.2019.40.4.340
  45. Park, Y., & Park, M. (2018). Exploring students competencies to be creative problem solvers with computational thinking practices. Journal of the Korean Earth Science Society, 39(4), 388-400. https://doi.org/10.5467/JKESS.2018.39.4.388
  46. Park, Y.-S., Jeong, D.-H., Yu, J.-Y., & Flick, L. (2015a). Application of computational thinking for activating STEAM education. Paper presented at the biannual meeting of Korean Earth Science Society, April 23-24, Chuncheon National University of Education, Chuncheon, Korea.
  47. Park, Y.-S., Hwang, J.-K., & Flick, L. B. (2015b). The analysis of computational thinking in STEAM program about climate change and water shortage and suggestions in science education. Paper presented at the biannual meeting of Korean Earth Science Society, September 10-11, Jeonbuk National University, Jeonju, Korea.
  48. Polya, G. (1957). How to solve it: A new aspect of mathematical method. Princeton, NJ: Princeton University Press.
  49. Rezba R., Fiel, R., & Sprague, C. (1995). Learning and assessing science process skills. 3rd Ed. Dubuque, Iowa : Kendall/Hunt.
  50. Rossman, J. (1931). The psychology of the inventor. Washington, DC: Inventor's Publishing Company.
  51. RSC2017 (2017). Robot software challenge pre-description. Retrieved from http://rsc.education/2017/ko/tutorial/briefing.jsp
  52. Sadler, T. D., Burgin, S., McKinney, L., & Ponjuan, L. (2010). Learning science through research apprenticeships: A critical review of the literature. Journal of Research in Science Teaching, 47(3), 235-256. https://doi.org/10.1002/tea.20326
  53. Selby, C., & Woollard, J. (2013). Computational thinking: the developing definition. ITiCSE conference paper. University of Kent, Canterbury, England.
  54. Selby, C. & Woollard, J. (2014). Refining an understanding of computational thinking. University of Southampton Institutional Repository. Retrieved from http://eprints.soton.ac.uk/id/eprint/372410
  55. Shin, S., Kim, C., Park, N., Kim, K., Seong, Y., & Jeong, Y. (2016). Convergence organization strategies of the computational thinking in informatics curriculums. Journal of the Korean Association of Information Education. 20(6), 607-616.
  56. Sneider, C., Stephenson, C., Schafer, B., & Flick, L. (2014). Computational thinking in high school science classroom. The Science Teacher, 81(5), 53-60.
  57. Song, H. (2007). Instructional Design Principles for Enhancing Creative Problem Solving Skills. The Korea Association of Yeolin Education, 15(3), 55-73.
  58. Todd, R. D., Todd, K. R., & McCrory, D. L. (1996). Introduction to design and technology. New York, NY: Thomson Learning Tools.
  59. Wallas, G. (1926). The art of thought. New York, NY: Harcourt Brace.
  60. Wecker, C., Rachel, A., Heran-Dorr, E., Waltner, C., Wiesner, H., & Fischer, F. (2013). Presenting theoretical ideas prior to inquiry activities fosters theory-level knowledge. Journal of Research in Science Teaching, 50(10), 1180-1206. https://doi.org/10.1002/tea.21106
  61. Weintrop, D., Beheshti, E., Horn, M., & Orton, K. (2015). Defining computational thinking for mathematics and science classroom. Journal of Science Education and Technology, 25, 127-147. https://doi.org/10.1007/s10956-015-9581-5
  62. Wing, J. M. (2006). Computational thinking. Communication of the ACM, 49(3), 33-35. https://doi.org/10.1145/1118178.1118215