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Educational Psychology Review

, Volume 21, Issue 1, pp 43–54 | Cite as

The Scientific Value of Cognitive Load Theory: A Research Agenda Based on the Structuralist View of Theories

  • Peter Gerjets
  • Katharina Scheiter
  • Gabriele Cierniak
Reflections on the Field

Abstract

In this paper, two methodological perspectives are used to elaborate on the value of cognitive load theory (CLT) as a scientific theory. According to the more traditional critical rationalism of Karl Popper, CLT cannot be considered a scientific theory because some of its fundamental assumptions cannot be tested empirically and are thus not falsifiable. According to the structuralist view of theories introduced by Joseph D. Sneed, a theory may be considered scientific even if it comprises nontestable fundamental assumptions. Rather, the scientific value of a theory results from the holistic empirical content of the overall theory net built around fundamental assumptions and from the successful applications of this theory net to explain and predict empirical findings. This latter view is helpful to explicate some implicit methodological assumptions of CLT research and to avoid the potential circularity of CLT’s fundamental assumptions. Additionally, the structuralist view of theories can be directly used to derive a research agenda for the future development of CLT.

Keywords

Critical rationalism Structuralist view of theories Theory validation Fallibility Cognitive load measurement Cognitive load theory 

References

  1. Atkinson, R. C., & Shiffrin, R. M. (1968). Human memory: A proposed system and its control processes. In K. W. Spence, & J. T. Spence (Eds.), The psychology of learning and motivation (Vol. 2, pp. 89–195). London: Academic.CrossRefGoogle Scholar
  2. Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16, 389–400. doi: 10.1016/j.learninstruc.2006.09.001.CrossRefGoogle Scholar
  3. Baddeley, A. D. (1999). Essentials of human memory. Hove, UK: Psychology.Google Scholar
  4. Balzer, W., Moulines, C. U., & Sneed, J. D. (1987). An architectonic for science. The structuralist program. Dordrecht: Reidel.Google Scholar
  5. Brünken, R., Plass, J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53–61. doi: 10.1207/S15326985EP3801_7.CrossRefGoogle Scholar
  6. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293–332. doi: 10.1207/s1532690xci0804_2.CrossRefGoogle Scholar
  7. Chandler, P., & Sweller, J. (1992). The split-attention effect as a factor in the design of instruction. The British Journal of Educational Psychology, 62, 233–246.Google Scholar
  8. Cierniak, G., Scheiter, K., & Gerjets, P. (2008). Explaining the split-attention effect: Is the reduction of extraneous cognitive load accompanied by an increase in germane cognitive load? Computers in Human Behavior, in press.Google Scholar
  9. Gerjets, P., & Scheiter, K. (2003). Goal configurations and processing strategies as moderators between instructional design and cognitive load: Evidence from hypertext-based instruction. Educational Psychologist, 38, 33–41. doi: 10.1207/S15326985EP3801_5.CrossRefGoogle Scholar
  10. Gerjets, P., Scheiter, K., & Schorr, T. (2003). Modeling processes of volitional action control in multiple-task performance: How to explain effects of goal competition and task difficulty on processing strategies and performance within Act-R. Cognitive Science Quarterly, 3, 355–400.Google Scholar
  11. Gerjets, P., Scheiter, K., Opfermann, M., Hesse, F. W., & Eysink, T. H. S. (2008a). Learning with hypermedia: The influence of representational formats and different levels of learner control on performance and learning behavior. Computers in Human Behavior, in press.Google Scholar
  12. Gerjets, P., Scheiter, K., & Schuh, J. (2008b). Information comparisons in example-based hypertext environments: Supporting learners with processing prompts and an interactive comparison tool. Educational Technology Research and Development, 56, 73–92. doi: 10.1007/s11423-007-9068-z.CrossRefGoogle Scholar
  13. Halford, G. S., Wilson, W. H., & Phillips, S. (1998). Processing capacity defined by relational complexity: Implications for comparative, developmental and cognitive psychology. The Behavioral and Brain Sciences, 21, 803–831.PubMedGoogle Scholar
  14. Jonassen, D. H., Tessmer, M., & Hannum, W. H. (1999). Task analysis methods for instructional design. Mahwah, NJ: Erlbaum.Google Scholar
  15. Kester, L., Kirschner, P. A., & van Merriënboer, J. J. G. (2005). The management of cognitive load during complex cognitive skill acquisition by means of computer-simulated problem solving. The British Journal of Educational Psychology, 75, 71–85. doi: 10.1348/000709904X19254.PubMedCrossRefGoogle Scholar
  16. Paas, F. (1992). Training strategies for attaining transfer or problem solving skill in statistics: A cognitive load approach. Journal of Educational Psychology, 84, 429–434. doi: 10.1037/0022-0663.84.4.429.CrossRefGoogle Scholar
  17. Paas, F., Van Merrienboer, J. J. G., & Adam, J. J. (1994). Measurement of cognitive load in instructional research. Perceptual and Motor Skills, 79, 419–430.PubMedGoogle Scholar
  18. Popper, K. (1959). The logic of scientific discovery. New York, NY: Basic Books.Google Scholar
  19. Popper, K. (1963). Conjectures and refutations. London: Routledge.Google Scholar
  20. Scheiter, K., Gerjets, P., & Catrambone, R. (2006). Making the abstract concrete: Visualizing mathematical solution procedures. Computers in Human Behavior, 22, 9–26. doi: 10.1016/j.chb.2005.01.009.CrossRefGoogle Scholar
  21. Schnotz, W., & Kürschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469–508. doi: 10.1007/s10648-007-9053-4.CrossRefGoogle Scholar
  22. Sneed, J. D. (1979). The logical structure of mathematical physics (2nd ed.). Dordrecht: Reidel.Google Scholar
  23. Stegmüller, W. (1979). The structuralist view of theories. Berlin: Springer.Google Scholar
  24. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
  25. Sweller, J. (1993). Some cognitive processes and their consequences for the organisation and presentation of information. Australian Journal of Psychology, 45, 1–8. doi: 10.1080/00049539308259112.CrossRefGoogle Scholar
  26. Sweller, J., van Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296. doi: 10.1023/A:1022193728205.CrossRefGoogle Scholar
  27. Westermann, R. (1988). Structuralist reconstruction of psychological research: Cognitive dissonance. German Journal of Psychology, 12, 218–231.Google Scholar
  28. Westermann, R., Heise, E., & Gerjets, P. (1992). The justification of empirical suppositions. In H. Westmeyer (Ed.), The structuralist program in psychology: Foundations and applications (pp. 41–54). Bern: Hogrefe.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Peter Gerjets
    • 1
  • Katharina Scheiter
    • 2
  • Gabriele Cierniak
    • 3
  1. 1.Knowledge Media Research CenterTuebingenGermany
  2. 2.University of TuebingenTuebingenGermany
  3. 3.Knowledge Media Research CenterTuebingenGermany

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