# The Educational Value of Multiple-representations when Learning Complex Scientific Concepts

## Abstract

When people are learning complicated scientific concepts, interacting with multiple forms of representation such as diagrams, graphs and equations can bring unique benefits. Unfortunately, there is considerable evidence to show that learners often fail to exploit these advantages, and in the worse cases inappropriate combinations of representations can completely inhibit learning. In other words, multiple representations are powerful tools but like all powerful tools they need careful handling if learners are to use them successfully. In this chapter, I will review the evidence that suggests that multiple representations serve a number of important roles in science education. I will also consider why the research on the effectiveness of multiple representations shows that all too often they do not achieve their desired educational goals and I consider what can be done to overcome these problems.

## Keywords

Line Graph Multiple Representation Dynamic Graph High Prior Knowledge Educational Psychology Review## Preview

Unable to display preview. Download preview PDF.

## References

- Ainsworth, S., & Van Labeke, N. (2004). Multiple forms of dynamic representation.
*Learning and Instruction, 14*(3), 241–255.CrossRefGoogle Scholar - Ainsworth, S. E. (1999). The functions of multiple representations.
*Computers & Education, 33*(2–3), 131–152.CrossRefGoogle Scholar - Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations.
*Learning and Instruction, 16*(3), 183–198.CrossRefGoogle Scholar - Ainsworth, S. E., Bibby, P., & Wood, D. (2002). Examining the effects of different multiple representational systems in learning primary mathematics.
*Journal of the Learning Sciences, 11*(1), 25–61.CrossRefGoogle Scholar - Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications.
*Review of Research in Education, 24*, 61–100.Google Scholar - Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation in physics problems by experts and novices.
*Cognitive Science, 5*(2), 121–152.CrossRefGoogle Scholar - Cox, R. (1996).
*Analytical reasoning with multiple external representations.*University of Edinburgh Scotland: University of Edinburgh Press.Google Scholar - Dienes, Z. (1973).
*The six stages in the process of learning mathematics*. Slough: NFER-Nelson.Google Scholar - Elby, A. (2000). What students’ learning of representations tells us about constructivism.
*Journal of Mathematical Behavior, 19*, 481–502.CrossRefGoogle Scholar - Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications.
*Journal for Research in Mathematics Education, 32*(2), 124–158.CrossRefGoogle Scholar - Grossen, B., & Carnine, D. (1990). Diagramming a Logic Strategy –Effects on Difficult Problem Types and Transfer.
*Learning Disability Quarterly, 13*(3), 168–182.CrossRefGoogle Scholar - Heuer, D. (2002). PAKMA 2002 – Interactive simulation, measurement, reproduction, modelling and analysis in physics; CD-ROM. Hannover: Schroedel Verlag.Google Scholar
- Kaput, J. J. (1989). Linking representations in the symbol systems of algebra. In S. Wagner & C. Kieran (Eds.),
*Research issues in the learning and teaching of algebra*(pp. 167–194). Hillsdale, NJ: LEA.Google Scholar - Kozma, R., Chin, E., Russell, J., & Marx, N. (2000). The roles of representations and tools in the chemistry laboratory and their implications for chemistry learning.
*Journal of the Learning Sciences, 9*(2), 105–143.CrossRefGoogle Scholar - Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth 10000 words.
*Cognitive Science, 11*(1), 65–99.CrossRefGoogle Scholar - Leinhardt, G., Zaslavsky, O., & Stein, M. M. (1990). Functions, graphs, and graphing: tasks, learning and teaching.
*Review of Educational Research, 60*, 1–64.CrossRefGoogle Scholar - Mayer, R. E. (2001).
*Multimedia learning*. Cambridge: Cambridge University Press.Google Scholar - Ploetzner, R. (1995). The construction and coordination of complementary problem representations in physics.
*Journal of Artificial Intelligence in Education, 6*(2/3), 203–238.Google Scholar - Ploetzner, R., Fehse, E., Kneser, C., & Spada, H. (1999). Learning to relate qualitative and quantitative problem representations in a model-based setting for collaborative problem solving.
*Journal of the Learning Sciences, 8*(2), 177–214.CrossRefGoogle Scholar - Ploetzner, R., Lippitsch, S., Galmbacher, M., & Heuer, D. (2006). Students’ Difficulties in Learning Physics from Dynamic and Interactive Visualizations. In S. A. Barab, K. E. Hay, & D. T. Hickey (Eds.),
*Proceedings of the Seventh International Conference of the Learning Sciences (Vol 2) .*(Vol. 550–556). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar - Preece, J. (1993). Graphs are not straightforward. In T. R. G. Green, S. J. Payne, & G. C. van der Veer (Eds.),
*The psychology of computer use*(pp. 41–56). London: Academic Press.Google Scholar - Reed, S. K. (2006). Cognitive architectures for multimedia learning.
*Educational Psychologist, 41*(2), 87–98.CrossRefGoogle Scholar - Roth, W.-M., & Bowen, G. M. (2001). Professionals read graphs: A semiotic analysis.
*Journal for Research in Mathematics Education, 32*, 159–194.CrossRefGoogle Scholar - Russell, J., Kozma, R., Becker, D., & Susskind, T. (2000). SMV: Chem; Synchronized Multiple Visualizations in Chemistry. New York: John Wiley.Google Scholar
- Scanlon, E. (1998). How beginning students use graphs of motion. In M. W. Van Someren, P. Reimann, H. P. A. Boshuizen, & T. de Jong (Eds.),
*Learning with multiple representations*(pp. 9–40). Amsterdam: Elsevier Science.Google Scholar - Schnotz, W. (2002). Commentary – Towards an integrated view of learning from text and visual displays.
*Educational Psychology Review, 14*(1), 101–120.CrossRefGoogle Scholar - Schwartz, D. L., & Martin, T. (2004). Inventing to prepare for future learning: The hidden efficiency of encouraging original student production in statistics instruction.
*Cognition and Instruction,*22(2), 129–184.CrossRefGoogle Scholar - Seufert, T. (2003). Supporting coherence formation in learning from multiple representations.
*Learning and Instruction, 13*(2), 227–237.CrossRefGoogle Scholar - Spiro, R. J., & Jehng, J.-C. (1990). Cognitive flexibility and hypertext: Theory and technology for nonlinear and multi-dimensional traversal of complex subject matter. In D. Nix & R. J. Spiro (Eds.),
*Cognition, education and multi-media: Exploring ideas in high technology*. Hillsdale, NJ: LEA.Google Scholar - Stieff, M. (2005). Connected chemistry – A novel modeling environment for the chemistry classroom.
*Journal of Chemical Education, 82*(3), 489–493.CrossRefGoogle Scholar - Tabachneck, H. J. M., Leonardo, A. M., & Simon, H. A. (1994).
*How does an expert use a graph ? A model of visual & verbal inferencing in economics.*Paper presented at the 16th Annual Conference of the Cognitive Science Society, Georgia Institute of Technology, Atlanta, Georgia.Google Scholar - Tabachneck-Schijf, H. J. M., & Simon, H. A. (1998). Alternative representations of instructional material. In D. Peterson (Ed.),
*Forms of representation*(pp. 28–46). Exeter: Intellect Books.Google Scholar - van der Meij, J., & de Jong, T. (2006). Supporting students’ learning with multiple representations in a dynamic simulation-based learning environment.
*Learning and Instruction, 16*(3), 199–212.CrossRefGoogle Scholar - van Joolingen, W. R., & De Jong, T. (2003). SIMQUEST: Authoring educational simulations. In T. Murray, S. Blessing, & S. E. Ainsworth (Eds.),
*Tools for advanced technology learning environments*(pp. 1–32). Amsterdam: Kluwer Academic Publishers.Google Scholar - Van Labeke, N., & Ainsworth, S. (2001). Applying the DeFT framework to the design of multi-representational instructional simulations. In J. D. Moore, C. L. Redfield, & W. L. Johnson (Eds.),
*Proceedings of the 10th international conference on AI in education*(pp. 314–321). Amsterdam: IOS Press.Google Scholar - Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis.
*Educational Psychology Review, 17*(4), 285–325.CrossRefGoogle Scholar - Yerushalmy, M. (1991). Student perceptions of aspects of algebraic function using multiple representation software.
*Journal of Computer Assisted Learning, 7*, 42–57.Google Scholar