Educational Psychology Review

, Volume 22, Issue 2, pp 123–138 | Cite as

Element Interactivity and Intrinsic, Extraneous, and Germane Cognitive Load

Review Article

Abstract

In cognitive load theory, element interactivity has been used as the basic, defining mechanism of intrinsic cognitive load for many years. In this article, it is suggested that element interactivity underlies extraneous cognitive load as well. By defining extraneous cognitive load in terms of element interactivity, a distinct relation between intrinsic and extraneous cognitive load can be established based on whether element interactivity is essential to the task at hand or whether it is a function of instructional procedures. Furthermore, germane cognitive load can be defined in terms of intrinsic cognitive load, thus also associating germane cognitive load with element interactivity. An analysis of the consequences of explaining the various cognitive load effects in terms of element interactivity is carried out.

Keywords

Cognitive load theory Element interactivity Extraneous cognitive load Intrinsic cognitive load Germane cognitive load 

References

  1. Beckmann, J. (2010). Taming a beast of burden—On some issues with the conceptualisation and operationalisation of cognitive load. Learning and Instruction, 20, 250–264.CrossRefGoogle Scholar
  2. Brunken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53–61.CrossRefGoogle Scholar
  3. Brunken, R., Plass, J. L., & Leutner, D. (2004). Assessment of cognitive load in multimedia learning with dual-task methodology: Auditory load and modality effects. Instructional Science, 32, 115–132.CrossRefGoogle Scholar
  4. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293–332.CrossRefGoogle Scholar
  5. Chandler, P., & Sweller, J. (1996). Cognitive load while learning to use a computer program. Applied Cognitive Psychology, 10, 151–170.CrossRefGoogle Scholar
  6. Chi, M. T., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.Google Scholar
  7. Cooper, G., & Sweller, J. (1987). Effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79, 347–362.CrossRefGoogle Scholar
  8. Gerjets, P., Scheiter, K., & Catrambone, R. (2006). Can learning from molar and modular worked examples be enhanced by providing instructional explanations and prompting self-explanations? Learning and Instruction, 16, 104–121.CrossRefGoogle Scholar
  9. Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.CrossRefGoogle Scholar
  10. Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588.CrossRefGoogle Scholar
  11. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
  12. Leahy, W., & Sweller, J. (2005). Interactions among the imagination, expertise reversal, and element interactivity effects. Journal of Experimental Psychology: Applied, 11, 266–276.CrossRefGoogle Scholar
  13. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice Hall.Google Scholar
  14. Paas, F. (1992). Training strategies for attaining transfer of problem-solving skill in statistics: A cognitive-load approach. Journal of Educational Psychology, 84, 429–434.CrossRefGoogle Scholar
  15. Paas, F., & van Merrienboer, J. (1993). The efficiency of instructional conditions: An approach to combine mental-effort and performance measures. Human Factors, 35, 737–743.Google Scholar
  16. Paas, F., & van Merrienboer, J. (1994). Variability of worked examples and transfer of geometrical problem-solving skills: A cognitive-load approach. Journal of Educational Psychology, 86, 122–133.CrossRefGoogle Scholar
  17. Paas, F., Tuovinen, J. E., Tabbers, H., & Van Gerven, P. W. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.CrossRefGoogle Scholar
  18. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.CrossRefGoogle Scholar
  19. Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.CrossRefGoogle Scholar
  20. Renkl, A., & Atkinson, R. (2003). Structuring the transition from example study to problem solving in cognitive skills acquisition: A cognitive load perspective. Educational Psychologist, 38, 15–22.CrossRefGoogle Scholar
  21. Renkl, A., Atkinson, R., & Grosse, C. (2004). How fading worked solution steps works—a cognitive load perspective. Instructional Science, 32, 59–82.CrossRefGoogle Scholar
  22. Schnotz, W., & Kurschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469–508.CrossRefGoogle Scholar
  23. Sweller, J. (1994). Cognitive load theory, learning difficulty, and instructional design. Learning and Instruction, 4, 295–312.CrossRefGoogle Scholar
  24. Sweller, J. (2003). Evolution of human cognitive architecture. In B. Ross (Ed.), The psychology of learning and motivation (Vol. 43, pp. 215–266). San Diego: Academic Press.Google Scholar
  25. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31.CrossRefGoogle Scholar
  26. Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12, 185–233.CrossRefGoogle Scholar
  27. Sweller, J., Mawer, R. F., & Ward, M. R. (1983). Development of expertise in mathematical problem solving. Journal of Experimental Psychology: General, 112, 639–661.CrossRefGoogle Scholar
  28. Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load as a factor in the structuring of technical material. Journal of Experimental Psychology: General, 119, 176–192.CrossRefGoogle Scholar
  29. Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3, 257–287.CrossRefGoogle Scholar
  30. van Gog, T., & Paas, F. (2008). Instructional efficiency: Revisiting the original construct in educational research. Educational Psychologist, 43, 16–26.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.School of EducationUniversity of New South WalesSydneyAustralia

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