Leveraging on Assessment of Representational Competence to Improve Instruction with External Representations

  • Mounir R. Saleh
  • Kristy L. Daniel
Part of the Models and Modeling in Science Education book series (MMSE, volume 11)


External representations are often used to explain complex scientific concepts. These representations can augment learning outcomes to include the ability to engage in highly cognitive tasks, such as creating novel solutions to scientific problems. Nevertheless, interpreting these representations can be cognitively demanding for learners with low levels of representational competence especially those unfamiliar with the learning context. Moreover, the representational competence of an individual learner can change depending on the difficulty of a given task. Therefore, assessment practices that (1) differentiate among learners of different levels of representational competence as well as (2) gauge its change along tasks with hierarchical difficulty can be quite informative for designing instruction with external representations. In this chapter, we provide an example for how assessment can be designed to meet the two objectives. Given that representational competence is context specific, we demonstrate how the first objective can be met through developing a valid and reliable assessment instrument that can discriminate between learners of related but different majors. To satisfy the second objective, we suggest utilizing a continuum of problems based on the hierarchical cognitive orders in revised Bloom’s taxonomy. Potential benefits of such practices are discussed based on a case study that included 111 college students from three different institutes. Holistic and Item psychometric analyses are also detailed to further elucidate how failure in instructional intervention can result from lack of representational competence that, if not accounted for, efforts of redesign may prove fruitless.


  1. Aiken, L. R. (2003). Psychological testing and assessment. Boston: Allyn and Bacon.Google Scholar
  2. Anderson, L. W., Krathwohl, D. R., Airiasian, W., Cruikshank, K. A., Mayer, R. E., & Pintrich, P. R. (2001). A taxonomy for learning, teaching and assessing: A revision of Bloom’s taxonomy of educational outcomes: Complete edition.Google Scholar
  3. Barnea, N., & Yehudit, J. D. (2000). Computerized molecular modeling-the new technology for enhancing model perception among chemistry educators and learners. Chemistry Education Research and Practice, 1(1), 109–120.CrossRefGoogle Scholar
  4. Brennan, R. L. (1972). A generalized upper-lower item discrimination index. Educational and Psychological Measurement, 32, 289–303.CrossRefGoogle Scholar
  5. Brown, H. D., & Abeywickrama, P. (2004). Language assessment. Principles and Classroom Practices. White Plains: Pearson Education.Google Scholar
  6. Chang, M., Hwang, W. Y., Chen, M. P., & Mueller, W. (Eds.). (2011). Edutainment technologies. Educational games and virtual reality/Augmented reality applications: 6th International Conference on E-learning and Games, Edutainment 2011, Taipei, Taiwan, September 7–9, 2011, Proceedings (Vol. 6872). Springer.Google Scholar
  7. Dacosta, B. (2008). The effect of cognitive aging on multimedia learning (Doctoral dissertation, University of Central Florida Orlando, Florida).Google Scholar
  8. DeLeeuw, K. E., & Mayer, R. E. (2008). A comparison of three measures of cognitive load: Evidence for separable measures of intrinsic, extraneous, and germane load. Journal of Educational Psychology, 100, 223–234.CrossRefGoogle Scholar
  9. Doran, R. L. (1980). Basic measurement and evaluation of science instruction. National Science Teachers Association, 1742 Connecticut Ave., NW, Washington, DC 20009 (Stock No. 471–14764; no price quoted).Google Scholar
  10. Griffard, P. B. (2013). Deconstructing and decoding complex process diagrams in university biology. In Multiple representations in biological education (pp. 165–183). Netherlands: Springer.CrossRefGoogle Scholar
  11. Halverson, K. L., & Friedrichsen, P. (2013). Learning tree thinking: Developing a new framework of representational competence. In Multiple representations in biological education (pp. 185–201). Netherlands: Springer.CrossRefGoogle Scholar
  12. de Jong, T. (2010). Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science, 38(2), 105–134.CrossRefGoogle Scholar
  13. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19(4), 509–539.CrossRefGoogle Scholar
  14. Klahr, D., & Robinson, M. (1981). Formal assessment of problem-solving and planning processes in preschool children. Cognitive Psychology, 13(1), 113–148.CrossRefGoogle Scholar
  15. 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.CrossRefGoogle Scholar
  16. Kozma, R., & Russell, J. (2005). Students becoming chemists: Developing representational competence. In Visualization in science education (pp. 121–145). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  17. Madrid, R. I., Van Oostendorp, H., & Melguizo, M. C. P. (2009). The effects of the number of links and navigation support on cognitive load and learning with hypertext: The mediating role of reading order. Computers in Human Behavior, 25(1), 66–75.CrossRefGoogle Scholar
  18. Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive guide in multimedia learning. Journal of Educational Psychology, 93(2), 377.CrossRefGoogle Scholar
  19. Mayer, R. E. (2009). Multimedia learning (2nd ed.). New York: Cambridge University Press.CrossRefGoogle Scholar
  20. Mayer, R. E., Mathias, A., & Wetzell, K. (2002). Fostering understanding of multimedia messages through pre-training: Evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied, 8(3), 147.Google Scholar
  21. Moreno, R. (2004). Decreasing cognitive load for novice students: Effects of explanatory versus corrective feedback in discovery-based multimedia. Instructional Science, 32(1–2), 99–113.CrossRefGoogle Scholar
  22. Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments. Educational Psychology Review, 19(3), 309–326.CrossRefGoogle Scholar
  23. Paivio, A. (1986). Mental representations: A dual coding approach. Oxford, England: Oxford University Press.Google Scholar
  24. Patton, M. Q. (2002). Qualitative research and evaluation methods. John Wiley & Sons, Ltd.Google Scholar
  25. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12(1), 61–86.CrossRefGoogle Scholar
  26. Schönborn, K. J., & Bögeholz, S. (2013). Experts views on translation across multiple external representations. In Multiple representations in biological education (pp. 111–128). Dordrecht: Springer Netherlands.CrossRefGoogle Scholar
  27. Stull, A. T., & Mayer, R. E. (2007). Learning by doing versus learning by viewing: Three experimental comparisons of learner-generated versus author-provided graphic organizers. Journal of Educational Psychology, 99(4), 808.CrossRefGoogle Scholar
  28. Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12(3), 185–233.CrossRefGoogle Scholar
  29. Treagust, D. F., & Tsui, C. Y. (Eds.). (2013). Multiple representations in biological education. Dordrecht: Springer Netherlands.Google Scholar
  30. Treagust, D., & Tsui, C. (2014). General instructional methods and strategies. In N. Lederman & S. Abell (Eds.), Handbook of research in science education (1st ed., p. 312). New York: Routledge.Google Scholar
  31. Van Merriënboer, J. J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177.CrossRefGoogle Scholar
  32. Van Merriënboer, J. J., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learner’s mind: Instructional design for complex learning. Educational Psychologist, 38(1), 5–13.CrossRefGoogle Scholar
  33. Van Merriënboer, J. J., Kester, L., & Paas, F. (2006). Teaching complex rather than simple tasks: Balancing intrinsic and germane load to enhance transfer of learning. Applied Cognitive Psychology, 20(3), 343–352.CrossRefGoogle Scholar
  34. Zhao, N., Wardeska, J. G., McGuire, S. Y., & Cook, E. (2014). Metacognition: An effective tool to promote success in college science teaching. Journal of College Science Teaching, 43(4), 48–54.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Mounir R. Saleh
    • 1
  • Kristy L. Daniel
    • 2
  1. 1.Bahrain Teachers College, University of BahrainZallaqKingdom of Bahrain
  2. 2.Texas State UniversitySan MarcosUSA

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