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Development and Validation of Visual Representation Competence Taxonomy

과학 교수 학습을 위한 시각적 표상 능력의 교육목표 분류체계 개발 및 타당화

  • Received : 2018.02.02
  • Accepted : 2018.03.30
  • Published : 2018.04.30

Abstract

Various forms of visual representations enable scientific discovery and scientific reasoning when scientists conduct research. Similarly, in science education, visual representations are important as a means to promote students' understanding of science concepts and scientific thinking skills. To provide a framework that could facilitate the effective use of visual representations in science classroom and systemic science education research, a visual representation competence taxonomy (VRC-T) was developed in this study. VRC-T includes two dimensions: the type of visual representation, and the cognitive process of visual representation. The initial categories for each dimension were developed based on literature review. Then validation and revision was made by conducting teachers' workshop and survey to experts. The types of visual representations were grouped into 3 categories (descriptive, procedural, and explanative representations) and the cognitive processes were grouped into 3 categories (interpretation, integration, and construction). The sub categories of each dimension and the validation process would be explained in detail.

과학자들은 연구 과정에서 다양한 시각적 표상을 다양한 방식으로 활용한다. 과학 교수 학습 과정에서도 시각적 표상은 과학 개념을 가르치거나 이해하기 위한 수단으로, 또 학생의 과학적 사고를 촉진하고, 탐구 능력을 증진시키기 위한 도구로 활용될 수 있다. 본 연구에서는 과학 교수 학습 과정에서 효과적인 시각적 표상 활용을 촉진하고, 체계적인 과학교육 연구의 기초를 제공하고자 하는 목적으로 시각적 표상능력의 교육목표 분류체계(visual representation competence taxonomy: VRC-T)를 개발하였다. VRC-T는 시각적 표상 유형과 인지 과정, 2개 차원으로 구성하였고, 선행 연구를 고찰하여 초기 모형을 개발한 뒤 교사 중심의 타당도 검토 결과, 전문가 중심의 타당도 검토 결과를 반영하여 최종 모형을 개발하였다. 최종 모형에서 시각적 표상 유형은 크게 '기술적 표상', '과정적 표상', '설명적 표상'으로 구분하였으며, 시각적 표상의 인지 과정은 크게 '해석하기', '통합하기', '구성하기'로 구분하였다. VRC-T의 타당화 과정과 각 세부 범주의 개요 및 예시를 설명하였고 가능한 활용 방안을 제안하였다.

Keywords

References

  1. Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333(6046), 1096-1097. https://doi.org/10.1126/science.1204153
  2. Anderson, L. W., Krathwohl, D. R., Airiasian, W., Cruikshank, K. A., Mayer, R. E., Pintrich, P. R., Raths, J. & Wittrock, M. C. (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom's Taxonomy of educational objectives: Abridged edition. New York: Longman.
  3. Bloom, B. S. (1956). Taxonomy of educational objectives. Handbook I: Cognitive Domain. New York: David McKay Company. Inc.
  4. Bucat, B., & Mocerino, M. (2009). Learning at the sub-micro level: Structural representations. In Multiple representations in chemical education (pp. 11-29). Springer, Dordrecht.
  5. Bungum, B. (2008). Images of physics: an explorative study of the changing character of visual images in Norwegian physics textbooks. Nordic Studies in Science Education, 4(2), 132-141.
  6. Burton, L. (2004). Helping students become media literate. In Workshop's paper. Australian School Library Association (NSW) Inc. 5th State Conference.
  7. Chittleborough, G., & Treagust, D. F. (2007). Correct interpretation of chemical diagrams requires transforming from one level of representation to another. Research in Science Education, 38(4), 463-482. https://doi.org/10.1007/s11165-007-9059-4
  8. Churches, A. (2009). Bloom's digital taxonomy. Educational Origami, 4.
  9. Colin, P., Chauvet, F., & Viennot, L. (2002). Reading images in optics: Students' difficulties and teachers' views. International Journal of Science Education, 24(3), 313-332. https://doi.org/10.1080/09500690110078923
  10. Dimopoulos, K., Koulaidis, V., & Sklaveniti, S. (2003). Towards an analysis of visual images in school science textbooks and press articles about science and technology. Research in Science Education, 33(2), 189-216. https://doi.org/10.1023/A:1025006310503
  11. diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385-398. https://doi.org/10.1016/S0732-3123(01)00051-7
  12. Dori, Y. J., Tal, R.T., & Tsaushu, M. (2003). Teaching biotechnology through case studies: can we improve higher order thinking skills of nonscience majors? Science Education, 87(6), 767.793. https://doi.org/10.1002/sce.10081
  13. Evagorou, M., Erduran, S., & Mantyla, T. (2015). The role of visual representations in scientific practices: from conceptual understanding and knowledge generation to 'seeing' how science works. International Journal of STEM Education, 2(1), 11. https://doi.org/10.1186/s40594-015-0024-x
  14. Gilbert, J. K., & Treagust, D. F. (2009). Towards a coherent model for macro, submicro and symbolic representations in chemical education. In Multiple representations in chemical education (pp. 333-350). Springer, Dordrecht.
  15. Gooding, D. (2006). From phenomenology to field theory: Faraday’s visual reasoning. Perspectives on Science, 14(1), 40-65. https://doi.org/10.1162/posc.2006.14.1.4
  16. Hauenstein, A. D. (1998). A conceptual framework for educational objectives. University Press of America, Inc.
  17. Jho, H., Jo, K., & Yoon, H.-G. (2017). Analysis of middle school students’ visual representation competences for electric current. New Physics: Sae Mulli, 67(6), 714-724. https://doi.org/10.3938/NPSM.67.714
  18. Jo, K., Jho, H., & Yoon, H.-G. (2015) Analysis of visual representations related to electromagnetism in primary and secondary science textbooks. New Physics: Sae Mulli, 65(4), 343-357. https://doi.org/10.3938/NPSM.65.343
  19. Johnstone, A. H. (1993). The development of chemistry teaching: A changing response to changing demand. Journal of Chemical Education, 70(9), 701. https://doi.org/10.1021/ed070p701
  20. Kim, O.-N. (2006). The comparative analysis of educational taxonomies in cognitive domain. The Korea Educational Review, 12(2), 165-189.
  21. Kim, T.-S., & Kim, B.-K. (2002). The comparison of graphing abilities of pupils in grades 7 to 12 based on TOGS (The test of graphing in science). Journal of the Korean Association for Science Education, 22(4), 768-778.
  22. Klopfer, L. E. (1971). Evaluation of learning in science. In B. S. Bloom, J. T. Hastings & G. F. Madaus (Eds.), Handbook on formative and summative evaluation of student learning. New York: MaGraw-Hill.
  23. Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In J. K. Gilbert (Ed.), Visualizations in Science Education (pp. 121-146). Dordrecht, The Netherlands: Springer.
  24. Lee, J. (2011). Revisiting graphicacy: The roles of graphicacy in the digital era and tasks of geographic education. The Journal of the Korean Association of Geographic and Environmental Education, 19(1), 1-15.
  25. Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39-48. https://doi.org/10.1016/S0193-3973(99)00049-0
  26. Lynch, M. (2006). The production of scientific images: vision and re-vision in the history, philosophy, and sociology of science. In L Pauwels (Ed.), Visual cultures of science: rethinking representational practices in knowledge building and science communication (pp. 26-40). Lebanon, NH: Darthmouth College Press.
  27. Marzano, R. J. (2001). Designing a new taxonomy of educational objectives. Corwin Press, Inc.
  28. 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
  29. 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
  30. Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. SpringerPlus, 3(1), 184. https://doi.org/10.1186/2193-1801-3-184
  31. Moline, S. (1995). I see what you mean: Children at work with visual information. Teachers Pub Group Inc.
  32. 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
  33. Ozcelik, A. T., & McDonald, S. P. (2013). Preservice science teachers’ uses of inscriptions in science teaching. Journal of Science Teacher Education, 24(7), 1103-1132. https://doi.org/10.1007/s10972-013-9352-1
  34. Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian journal of psychology, 45(3), 255-287. https://doi.org/10.1037/h0084295
  35. Park, S., Kim, H., & Lee E.-H. (2014). An Analysis of students’ graphicacy in Korea based on the national assessment of educational achievement, from 2005 to 2007. Journal of the Korean Geographical Society, 44(3), 410-427.
  36. Postigo, Y., & Pozo, J. I. (2004). On the road to graphicacy: The learning of graphical representation systems. Educational Psychology, 24(5), 623-644. https://doi.org/10.1080/0144341042000262944
  37. Schwarz, CV, Reiser, BJ, Davis, EA, Kenyon, L, Acher, A, Fortus, D, et al. (2009). Developing a learning progression for scientific modeling: making scientific modeling accessible and meaningful for learners. Journal of Research in Science Teaching, 46(6), 632-654. doi:10.1002/tea.20311.
  38. Talanquer, V. (2011). Macro, submicro, and symbolic: the many faces of the chemistry "triplet". International Journal of Science Education, 33(2), 179-195. https://doi.org/10.1080/09500690903386435
  39. Tippett, C. D. (2016) What recent research on diagrams suggests about learning with rather than learning from visual representations in science, International Journal of Science Education, 38(5), 725-746. https://doi.org/10.1080/09500693.2016.1158435
  40. Topsakal, U. U., & Oversby, J. (2013). What do scientist and non-scientist teachers notice about biology diagrams? Journal of Biological Education, 47(1), 21-28. https://doi.org/10.1080/00219266.2012.753102
  41. Waldrip, B., Prain, V., & Carolan, J. (2010). Using multi-modal representations to improve learning in junior secondary science. Research in Science Education, 40(1), 65-80. https://doi.org/10.1007/s11165-009-9157-6
  42. Yoon, H.-G. Jo, K., & Jho, H. (2016). Middle school students’ interpretation, construction, and application of visual representations for electrostatic induction. New Physics: Sae Mulli, 66(5), 580-589. https://doi.org/10.3938/NPSM.66.580
  43. Yoon, H.-G., Jo, K., & Jho, H. (2017). Secondary teachers’ perception about and actual use of visual representations in the teaching of electromagnetism. Journal of the Korean Association for Science Education, 37(2), 253-262. https://doi.org/10.14697/JKASE.2017.37.2.0253

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