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그림자 현상에 대한 초등학생의 시각적 표상 능력

Analysis of Elementary School Students' Visual Representation Competence for Shadow Phenomenon

  • 투고 : 2019.02.19
  • 심사 : 2019.03.26
  • 발행 : 2019.04.30

초록

본 연구와 관련된 선행 연구에서는 과학 교수 학습 과정에서 효과적인 시각적 표상 활용과 연구를 촉진하기 위한 목적으로 2개 차원으로 구성된 시각적 표상 능력의 교육목표 분류체계(visual representation competence taxonomy: VRC-T)가 개발되었다. 본 연구에서는 이러한 VRC-T에 기초하여 그림자 현상에 대한 초등학생의 시각적 표상 능력을 조사하고 그림자에 대한 과학 지식과 표상 능력 사이의 관계 및 VRC-T 인지 과정의 위계 관계를 탐색하고자 하였다. 연구 결과 그림자 현상에 대한 초등학생의 시각적 표상 능력을 '해석하기', '통합하기', '구성하기'의 대범주로 나누어 보면 대체적으로 '해석하기'가 가장 점수가 높고, 다음이 '구성하기', '통합하기'의 순으로 나타났다. 또 학생들이 정규 교육과정에서 그림자 관련 단원을 학습한 이후임에도 불구하고 시각적 표상 능력은 높지 않은 것으로 나타났다. 한편 텍스트 기반의 과학 지식은 시각적 표상 능력의 모든 범주와 상관이 높지 않았다. 이것은 텍스트 형식의 과학 지식을 가지고 있더라도 시각적 표상 능력은 갖추어져 있지 않을 가능성이 크다는 것과 과학 수업에서 시각적 표상을 좀 더 강조하여 다루어야 할 필요성을 나타낸다. 마지막으로 서열화 이론에 따라 그림자 현상에 대한 시각적 표상 능력의 인지 과정 위계 관계를 탐색한 결과, 인정비율을 다소 느슨하게 하는 경우 6개 인지 과정 사이에 일직선의 위계 관계가 발견되었다. 이것은 평가 도구나 과제, 시각적 표상 능력을 지도하는 수업 활동을 계획할 때 VRC-T가 유용하게 활용될 수 있는 분석틀임을 시사한다.

In previous study, visual representation competence taxonomy (VRC-T), which is composed of two dimensions, was developed for the purpose of promoting effective visual representation use and research in science education. In this study, elementary school students' visual representation competence for shadow phenomenon was investigated using VRC-T. In terms of visual representation competence, 'interpretation' was the highest score, followed by 'construction' and 'integration'. It also showed that students' visual representation competence was not high even after learning shadow-related units in the regular curriculum. On the other hand, text-based scientific knowledge was not correlated with all categories of visual representation competence. This indicates that there is a need to emphasize visual representation more in science class. Finally, hierarchical relationship among cognitive processes of VRC-T was explored according to ordering theory. If the tolerance level is somewhat loosened, a linear hierarchical relationship was found between the six cognitive processes. This suggests that VRC-T is an analytical framework that can be useful when designing assessment tools, tasks, and science class activities to enhance visual representation competence.

키워드

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Figure 1. The sequence between the two questions

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Figure 2. Question for interpreting

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Figure 3. Transforming the given representation into a different context

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Figure 4. Questionfor evaluating

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Figure 5. Drawing the features of observed phenomena

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Figure 6. Drawing to present one’s scientific idea

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Figure 7. Examples of inconsistent reponses between construction and evaluation of visual representation for shadow

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Figure 8. Hierarchy diagram of VRC-T cognitive processes

Table 1. Structure of the survey questionnaire

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Table 2. Analytical criteria for visual representation competence for shadow phenomenon

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Table 3. Scores of visual representation competence for shadow phenomenon

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Table 4. Responses on science knowledge test items

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Table 5. Correlations among VRC-T cognitive processes and science knowledge on shadow

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Table 6. Dichotomous binary response pattern

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Table 7. Response matrix table

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참고문헌

  1. Bae, K. A. (2005). Study of the elementary school students' conceptual change on the light through the cognitive conflict instruction. Master's Thesis, Seoul National University of Education.
  2. Carolan, J., Prain, V., & Waldrip, B. (2008). Using representations for teaching and learning in science. Teaching Science, 54(1), 18-23.
  3. Chi, M. T., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5(2), 121-152. https://doi.org/10.1207/s15516709cog0502_2
  4. Daniel, K. L., Bucklin, C. J., Leone, E. A., & Idema, J. (2018). Towards a Definition of Representational Competence. In Towards a Framework for Representational Competence in Science Education (pp. 3-11). Springer, Cham.
  5. Elkins, J., McGuire, K., Burns, M., Chester, A., & Kuennen, J. (2012). Theorizing visual studies: writing through the discipline. New York, NY: Routledge.
  6. Feher, E., & Rice, K. (1988). Shadows and anti-images: Children's conceptions of light and vision. II. Science Education, 72(5), 637-649. https://doi.org/10.1002/sce.3730720509
  7. Felten, P. (2008). Visual literacy. Change: The magazine of higher learning, 40(6), 60-64. https://doi.org/10.3200/CHNG.40.6.60-64
  8. Gilbert, J. K. (2005). Visualization: A metacognitive skill in science and science education. In Visualization in science education (pp. 9-27). Springer, Dordrecht.
  9. Hutchins, E. (1995). How a cockpit remembers its speeds. Cognitive science, 19(3), 265-288. https://doi.org/10.1207/s15516709cog1903_1
  10. Kozma, R. B., & Russell, J. (1997). Multimedia and understanding: Expert and novice responses to different representations of chemical phenomena. Journal of research in science teaching, 34(9), 949-968. https://doi.org/10.1002/(SICI)1098-2736(199711)34:9<949::AID-TEA7>3.0.CO;2-U
  11. Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualizations in Science Education (pp. 121-146). Springer, Dordrecht.
  12. Lemke, J. (2004). The literacies of science. In E. W. Saul (Ed.), Crossing borders in literacy and science instruction: Perspectives on theory and practice (pp. 33-47). Newark: International Reading Association/National Science Teachers Association
  13. Lim, C. H. (1992). Methods and procedures of ordering theory and hierarchical analysis of science process skills using ordering theory. Journal of the Korean Association for Science Education, 12(3), 91-107.
  14. Mayer, R. E. (2003). The promise of multimedia learning: using the same instructional design methods across different media. Learning and instruction, 13(2), 125-139. https://doi.org/10.1016/S0959-4752(02)00016-6
  15. McKenzie, D. L., & Padilla, M. J. (1986). The construction and validation of the test of graphing in science (TOGS). Journal of Research in Science Teaching, 23(7), 571-579. https://doi.org/10.1002/tea.3660230702
  16. Ministry of Education. (2015). 2015 revised curriculum: Science. Seoul: Ministry of Education.
  17. Nitz, S., & Tippett, C. D. (2012). Measuring representational competence in science. In E. de Vries & K. Scheiter (Eds.), Proceedings of EARLI SIG 2 Meeting, Staging knowledge and experience: How to take an advantage of representational technologies in education and training? (pp. 163-165), Grenoble, France.
  18. Nitz, S., Ainsworth, S., Nerdel, C., & Prechtl, H. (2014). Do student perceptions of teaching predict the development of representational competence and biological knowledge? Learning & Instruction, 31, 13-22. https://doi.org/10.1016/j.learninstruc.2013.12.003
  19. Oh, P. S. (2017). An interpretation of modeling-based elementary science lessons from a perspective of distributed cognition. Journal of Korean Elementary Science Education, 36(1), 16-30. https://doi.org/10.15267/keses.2017.36.1.016
  20. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian journal of psychology, 45(3), 255-287. https://doi.org/10.1037/h0084295
  21. Pande, P., & Chandrasekharan, S. (2017). Representational competence: Towards a distributed and embodied cognition account. Studies in Science Education, 53(1), 1-43. https://doi.org/10.1080/03057267.2017.1248627
  22. Park, S-T., Byun, D-W., Lee, H-B., Kim, J-T., & Yuk, K-C. (2005). A look at the physics concept hierarchy of pre-service physics teacher through the knowledge state analysis method. Journal of the Korean Association for Science Education, 25(7), 746-753.
  23. Park, S-Y., Park, J-H., & Back, N-G. (2014). An investigation on the conception of the light and shadow for the elementary students. The Journal of Korea Elementary Education, 25(3), 111-126.
  24. Pauwels, L. (2006). Visual cultures of science. Rethinking Representational Practices in Knowledge Building and Science Communication. Hanover, NH.
  25. Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In G. Salomon (Ed.), Distributed cognitions: Psychological and Educational Considerations (pp. 47-87). New York, NY: Cambridge University Press.
  26. Peirce, C. (1931). Logic as semiotic: The theory of signs. In Buchler Justus (Ed.), Philosophical writings of Peirce (1893-1910) (pp. 98-119). New York: Dover. Reprint 1955.
  27. Scheid, T., Mueller, A., Hettmannsperger, R., & Schnotz, W. (2013) Fostering the understanding of scientific experiments and phenomena through representational analysis tasks. The Proceedings of 2013 European Science Education Research Association, edited by C. P. Constantinou, N. Papadouris and A. Hadjigeorgiou (Nicosia, 2013). pp. 102-108.
  28. Yoon, H.-G. (2018). Development and validation of visual representation competence taxonomy. Journal of the Korean Association for Science Education, 38(2), 161-170. https://doi.org/10.14697/JKASE.2018.38.2.161