Browse > Article
http://dx.doi.org/10.14697/jkase.2018.38.2.161

Development and Validation of Visual Representation Competence Taxonomy  

Yoon, Hye-Gyoung (Chuncheon National University of Education)
Publication Information
Journal of The Korean Association For Science Education / v.38, no.2, 2018 , pp. 161-170 More about this Journal
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.
Keywords
Visual representation; Taxonomy; Type of visual representation; Cognitive process of visual representation;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 Bucat, B., & Mocerino, M. (2009). Learning at the sub-micro level: Structural representations. In Multiple representations in chemical education (pp. 11-29). Springer, Dordrecht.
2 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.
3 Burton, L. (2004). Helping students become media literate. In Workshop's paper. Australian School Library Association (NSW) Inc. 5th State Conference.
4 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.   DOI
5 Churches, A. (2009). Bloom's digital taxonomy. Educational Origami, 4.
6 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.   DOI
7 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.   DOI
8 diSessa, A. A., & Sherin, B. L. (2000). Meta-representation: An introduction. The Journal of Mathematical Behavior, 19(4), 385-398.   DOI
9 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.   DOI
10 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.   DOI
11 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.
12 Gooding, D. (2006). From phenomenology to field theory: Faraday’s visual reasoning. Perspectives on Science, 14(1), 40-65.   DOI
13 Hauenstein, A. D. (1998). A conceptual framework for educational objectives. University Press of America, Inc.
14 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.   DOI
15 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.   DOI
16 Johnstone, A. H. (1993). The development of chemistry teaching: A changing response to changing demand. Journal of Chemical Education, 70(9), 701.   DOI
17 Kim, O.-N. (2006). The comparative analysis of educational taxonomies in cognitive domain. The Korea Educational Review, 12(2), 165-189.
18 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.   DOI
19 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.
20 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.   DOI
21 Paivio, A. (1991). Dual coding theory: Retrospect and current status. Canadian journal of psychology, 45(3), 255-287.   DOI
22 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.
23 Postigo, Y., & Pozo, J. I. (2004). On the road to graphicacy: The learning of graphical representation systems. Educational Psychology, 24(5), 623-644.   DOI
24 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.   DOI
25 Talanquer, V. (2011). Macro, submicro, and symbolic: the many faces of the chemistry "triplet". International Journal of Science Education, 33(2), 179-195.   DOI
26 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.
27 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.   DOI
28 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.   DOI
29 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.
30 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.
31 Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39-48.   DOI
32 Mnguni, L. E. (2014). The theoretical cognitive process of visualization for science education. SpringerPlus, 3(1), 184.   DOI
33 Ainsworth, S., Prain, V., & Tytler, R. (2011). Drawing to learn in science. Science, 333(6046), 1096-1097.   DOI
34 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.
35 Marzano, R. J. (2001). Designing a new taxonomy of educational objectives. Corwin Press, Inc.
36 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.   DOI
37 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.   DOI
38 Moline, S. (1995). I see what you mean: Children at work with visual information. Teachers Pub Group Inc.
39 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.   DOI
40 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.   DOI
41 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.   DOI
42 Bloom, B. S. (1956). Taxonomy of educational objectives. Handbook I: Cognitive Domain. New York: David McKay Company. Inc.
43 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.