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알고리즘 표현의 실험 안내 자료 개발 - 자석의 성질 실험을 중심으로 -

Development of Experimental Guide Materials for Algorithmic Expression - Focusing on Magnetic Properties Experiment -

  • 투고 : 2021.03.28
  • 심사 : 2021.05.20
  • 발행 : 2021.08.31

초록

본 연구에서는 컴퓨팅 사고의 핵심인 알고리즘 표현을 실험 활동에 적용할 수 있도록 교사를 위한 실험 안내 자료를 개발하였다. 텍스트로 제시된 실험 매뉴얼을 순서도 기호를 사용하여 정보 시각화 프로세스에 따라 직선형, 분기형, 반복형 구조의 알고리즘 형태로 변환하였다. 그 예시로, 자석의 성질을 알아보는 실험을 알고리즘 표현을 적용하여 실험 안내 자료를 개발하였다. 개발된 실험 안내 자료는 정보의 적합성과 판단 여부가 표현된 분기 및 반복의 알고리즘 구조를 가지고 있다는 점과 실험 과정을 시각화하여 표현했다는 측면에서 기존의 순차적으로 표현된 실험 안내 자료와 차이가 있다. 본 연구에서 개발된 실험 안내 자료는 교사들의 알고리즘 사고에 대한 이해와 이를 적용한 실험 실행에 도움을 줄 수 있을 것이라 기대된다.

In this study, experimental guide materials for teachers were developed so that algorithm expression, the core of computational thinking, can be applied to experimental activities. The experimental manuals presented in text was converted into an algorithmic form with a linear, branched, and repetitive structure according to the information visualization process using flowchart symbols. As an example, an experiment guide materials was developed by applying an algorithm expression to an experiment to find out the properties of a magnet. The developed experiment guide materials is different from the existing experiment guide materials expressed only sequentially in that it has an algorithmic structure of branching and repetition in which the suitability and judgment of information are expressed, and that the experiment process is visualized and expressed. It is expected that the experimental guide materials developed in this study will help teachers to understand algorithmic thinking and to implement experiments using it.

키워드

참고문헌

  1. Ashby, W. R. (1956). An introduction to cybernetics. Chapman and Hall. Retrieved from http://pcp.vub.ac.be/books/IntroCyb.pdf
  2. Atwood, R. K., Christopher, J. E., Combs, R. K., & Roland, E. A. E. (2010). In-service elementary teachers' understanding of magnetism concepts before and after non-traditional instruction. Science Educator, 19(1), 64-76.
  3. Barrow, L. H. (1987). Magnet concepts and elementary students' misconceptions. In J. Novak (ed), Proceedings of the second international seminar on misconceptions and educational strategies in science and mathematics, (pp. 17-22). Cornell University Press.
  4. Barrow, L. H. (2000). Do elementary science methods textbooks facilitate the understanding of magnet concepts? Journal of Science Education and Technology, 9(3), 199-205. https://doi.org/10.1023/A:1009487432316
  5. Barwise, J., & Etchemendy, J. (1991). Visual information and valid reasoning. In W. Zimmerman & S. Cunningham (eds), Visualization in teaching and learning mathematics (pp. 9-24). Mathematical Association of America.
  6. Bederson, B. B., & Shneiderman, B. (2003). The craft of information visualization: Readings and reflections. Morgan Kaufmann Publishers.
  7. Bell, T., Witten, I., & Fellows, M. (2015). Computer science unplugged. Retrieved from http://csunplugged.org/wp-content/uploads/2015/03/CSUnplugged_OS_2015_v3.1.pdf
  8. Bennett, V., Koh, K., & Repenning, A. (2013). Computing creativity: Divergence in computational thinking. Proceeding of the 44th ACM Technical Symposium on Computer Science Education, 359-364.
  9. Bezu, Z., Menberu, W., & Asrat, M. (2016). Improving the implementation of pre-laboratory flow chart, cooperative learning and laboratory report writing in first year organic chemistry laboratory class. African Journal of Chemical Education, 6(1), 47-64.
  10. Branch, R. M., Erika Mane, C., & Shin, M. Y. (2018). Effect of graphic element type on visual perceptions of curvilinear and rectilinear flow diagrams. Journal of Visual Literacy, 37(2), 119-136. https://doi.org/10.1080/1051144x.2018.1493249
  11. Chittaro, L. (2006). Visualizing information on mobile devices. ACM Computer, 39(3), 40-45.
  12. Choi, B. G., & Jeon, Y. S. (2016). Analysis of abnormalities of magnet poles in an elementary science classroom. New Physics: Sae Mulli, 66(7), 893-899. https://doi.org/10.3938/NPSM.66.893
  13. Choi, H. (2016). Developing pre-service teachers' computational thinking: Analysis of the five core CT competencies. Journal of the Korean Association of Information Education, 20(6), 553-562.
  14. Davidowitz, B., Rollnick, M., & Fakudze, C. (2005). Development and application of a rubric for analysis of novice students' laboratory flow diagrams. International Journal of Science Education, 27(1), 43-59. https://doi.org/10.1080/0950069042000243754
  15. Dechsri. P., Jones. L., & Heikkinen. H. (1997). Effect of a laboratory manual design incorporating visual information-processing aids on student learning and attitudes. Journal of Research in Science Teaching, 34(9), 891-904. https://doi.org/10.1002/(SICI)1098-2736(199711)34:9<891::AID-TEA4>3.0.CO;2-P
  16. Domin, D. S. (1999). A review of laboratory instruction styles. Journal of Chemical Education, 76(4), 543-547. https://doi.org/10.1021/ed076p543
  17. Go, B. O. (2012). A study on problem solving method utilizing algorithm (flowchart). The Journal of Education Studies, 49(1), 103-118.
  18. Gwon, O. J. (2015). A study on the perception of elementary school teachers on errors occurred in elementary school science classes. The Journal of Education Studies, 52(1), 45-60.
  19. Han, O. Y., & Kim, J. H. (2011). Development of a teaching-learning model for effective algorithm education. The Journal of Korean Association of Computer Education, 14(2), 13-22. https://doi.org/10.32431/KACE.2011.14.2.002
  20. Hodson, D. (1990). A critical look at practical work in school science. School Science Review, 71(256), 33-40.
  21. Hofstein, A., & Lunetta, V. N. (2004). The laboratory in science education: Foundations for the twenty-first century. Science Education, 88(1), 28-54. https://doi.org/10.1002/sce.10106
  22. Hong, S. I., & Lee, J. H. (2012). Children's conceptual ecologies in the big-concept-based learning about permanent magnets. New Physics: Sae Mulli, 62(6), 572-583. https://doi.org/10.3938/NPSM.62.572
  23. Hwang, Y. H., Moon, G. J., & Choi, Y. H. (2020). Analysis of students' computational thinking competencies and their changes through computational thinking-based socioscientific issues (CT-SSI) educational programs. Journal of Education & Culture, 26(2), 175-196. https://doi.org/10.24159/JOEC.2020.26.2.175
  24. Hwang, Y. H., Moon, G. J., & Park, Y. B. (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
  25. Im, J. G., Lee, S. R., Kim, J. Y., & Yang, I. H. (2010). An analysis on the factors that causes the difference between teachers and students on the perception of the laboratory class aims in elementary school. Journal of Science Education, 34(2), 359-368. https://doi.org/10.21796/jse.2010.34.2.359
  26. Jang, H. S., & Oh, W. G. (2009). Secondary students' misconceptions about magnetic fields and magnetic materials. New Physics: Sae Mulli, 58(6), 629-637.
  27. Jeon, B. H. (1996). Mainly about 'magnet' subjects matter selection and teaching in the primary science education. The Journal of the Institute of Science Education, 17(1), 171-190.
  28. Jeon, Y., & Kim, T. (2016). Suggestions of instructional strategy in the affective aspect through the analysis of causality between the computer learning attitude factors of the non-major students in the software education class of the teacher training college. Journal of Korean Association of Computer Education, 22(1), 169-172.
  29. Jeong, Y. S. (2014). A study on the content framework of algorithm education in primary and middle schools. Journal of the Korean Association of Information Education, 18(2), 275-284. https://doi.org/10.14352/jkaie.2014.18.2.275
  30. Kang, E. J., & Kim, J. N. (2020). The effects of experimental activity with computing thinking expression on elementary school students' scientific models. New Physics: Sae Mulli, 70(7), 595-602. https://doi.org/10.3938/NPSM.70.595
  31. Khan, M., & Khan, S. S. (2011). Data and information visualization methods, and interactive mechanisms: A survey. International Journal of Computer Applications, 34(1), 1-14. https://doi.org/10.5120/2004-2701
  32. Kim, H. S., & Choi, S. Y. (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
  33. Kim, K. S. (2016). A recognition analysis of elementary teachers for software education of 2015 revised Korea curriculum. Journal of the Korea Association of Information Education, 20(1), 47-56. https://doi.org/10.14352/jkaie.2016.20.1.47
  34. Kim, M. Y., & Kim, S. W. (2020). The effect of scientific problem-solving education using physical computing on computational thinking. Journal of Learner-Centered Curriculum and Instruction, 20(8), 387-410. https://doi.org/10.22251/jlcci.2020.20.8.387
  35. Lee, H. C., & Jeong, S. H. (2004). An investigation of pre-service teachers' understandings on magnet. Journal of Korean Elementary Science Education, 23(2), 141-151.
  36. Lee, H. J., Lee, Y. S., Lee, H. I., Kang, H. G., & Kim, J. B. (2017). Factors affecting on strength and polarity of magnetization in textbook for elementary school. School Science Journal, 11(1), 59-66. https://doi.org/10.15737/SSJ.11.1.201702.59
  37. Lee, H. W. (2008). A study on the education effect of algorithm identification. Master's thesis, Graduate School of Education, Kyunghee University.
  38. Lee, J. H., & Hur, K. (2010). Development of elementary robot programming problems using algorithmic thinking-based problem model. Journal of the Korean Association of Information Education, 14(2), 189-197.
  39. Lee, Y. B., & Park, J. E. (2012). Pedagogical methodology of teaching activity-based flow chart for elementary school students. Journal of the Korean Association of Information Education, 16(4), 489-501.
  40. Lohse, G. L., Biolsi, K., Walker, N., & Rueter, H. H. (1994). A classification of visual representations. Communications of the ACM, 37(12), 36-50. https://doi.org/10.1145/198366.198376
  41. Mayer, R. E. (1994). Visual aids to knowledge construction: Building mental representations from pictures and words. In W. K. Schnotz (ed), Comprehension of graphics (pp. 125-138). North Holland.
  42. Ministry of Education [MOE]. (2015a). National practical course/computer and information curriculum No. 2015-74.
  43. Ministry of Education [MOE]. (2015b). National science curriculum No. 2015-74.
  44. Shim, J. K. (2018). Analysis of teacher's ICT literacy and level of programming ability for SW education. KIPS Transactions on Computer and Communication Systems, 7(4), 91-98. https://doi.org/10.3745/KTCCS.2018.7.4.91
  45. Silvester, K. J., & O'Neill, J. C. (2019). Manufacturing cost flow diagrams as an alternative method of external problem representation -A diagrammatic approach to teaching cost accounting and evidence of its effectiveness. Journal of Higher Education Theory and Practice, 16(2), 81-104.
  46. Smolleck, L., & Hershberger, V. (2011). Playing with science: An investigation of young children's science conceptions and misconceptions. Current Issues in Education, 14(1), 1-32.
  47. Song, J. W., Kim, I. G., Kim, Y. M., Gwon, S. G., Oh, W. G., & Park, J. W. (2004). Instruction of students' misconceptions in physics [학생의 물리 오개념 지도]. Books Hill. pp. 125-127.
  48. Sullivan, D. O., & Igoe, T. (2004). Physical computing: Sensing and controlling the physical world with computers. Course Technology Press Communications of the ACM, 52(6), 28-30.
  49. Wertheimer, M. (1922). Untersuchungen zur Lehre von der Gestalt. Psychologische Forschung, 1(1), 47-58. https://doi.org/10.1007/BF00410385
  50. Wing, J. M. (2017). Computational thinking's influence on research and education for all. Italian Journal of Educational Technology, 25(2), 7-14.
  51. Yoo, J. H., & Kim, J. H. (2008). A conceptual study on computational thinking in problem-solving process. The Journal of Creative Informatics & Computing Education, 2(2), 15-24.
  52. Yoon, M. R., Park, J. S., & Park, I. W. (2017). Elementary school teachers' perception on the compass making activity and improving and application of the activity. Korean Journal of Elementary Education, 28(3), 49-59. https://doi.org/10.20972/kjee.28.3.201709.49