Instructional Science

, Volume 32, Issue 1–2, pp 33–58 | Cite as

Designing Instructional Examples to Reduce Intrinsic Cognitive Load: Molar versus Modular Presentation of Solution Procedures

  • Peter Gerjets
  • Katharina Scheiter
  • Richard Catrambone

Abstract

It is usually assumed that successful problemsolving in knowledge-rich domains depends onthe availability of abstract problem-typeschemas whose acquisition can be supported bypresenting students with worked examples.Conventionally designed worked examples oftenfocus on information that is related to themain components of problem-type schemas, namelyon information related to problem-categorymembership, structural task features, andcategory-specific solution procedures. However,studying these examples might be cognitivelydemanding because it requires learners tosimultaneously hold active a substantial amountof information in working memory. In ourresearch, we try to reduce intrinsic cognitiveload in example-based learning by shifting thelevel of presenting and explaining solutionprocedures from a `molar' view – that focuseson problem categories and their associatedoverall solution procedures – to a more`modular' view where complex solutions arebroken down into smaller meaningful solutionelements that can be conveyed separately. Wereview findings from five of our own studiesthat yield evidence for the fact thatprocessing modular examples is associated witha lower degree of intrinsic cognitive load andthus, improves learning.

cognitive load cognitive skill acquisition example design schema acquisition worked examples 

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REFERENCES

  1. Atkinson, R.K., Catrambone, R. & Merrill, M.M. (2003) Aiding transfer in statistics: Examining the use of conceptually oriented equations and elaborations during subgoal learning, Journal of Educational Psychology 95(4): 762–773.Google Scholar
  2. Atkinson, R.K., Derry, S.J., Renkl, A. & Wortham, D.W. (2000) Learning from examples: Instructional principles from the worked examples research, Review of Educational Research 70(2): 181–214.Google Scholar
  3. Catrambone, R. (1994) Improving examples to improve transfer to novel problems, Memory and Cognition 22(5): 606–615.Google Scholar
  4. Catrambone, R. (1998) The subgoal learning model: Creating better examples to improve transfer to novel problems, Journal of Experimental Psychology: General 127(4): 355–376.Google Scholar
  5. Catrambone, R. & Holyoak, K.J. (1989) Overcoming contextual limitations on problemsolving transfer, Journal of Experimental Psychology: Learning, Memory, and Cognition 15(6): 1147–1156.Google Scholar
  6. Chi, M.T.H., Bassok, M., Lewis, M., Reimann, P. & Glaser, R. (1989) Self-explanations: How students study and use examples in learning to solve problems, Cognitive Science 13(2): 145–182.Google Scholar
  7. Chi, M.T.H., de Leeuw, N., Chiu, M.-H. & LaVancher, C. (1994) Eliciting self-explanations improves understanding, Cognitive Science 18(3): 439–477.Google Scholar
  8. Cummins, D.D. (1992) Role of analogical reasoning in the induction of problem categories, Journal of Experimental Psychology: Learning, Memory, and Cognition 18(5): 1103–1124.Google Scholar
  9. Derry, S.J. (1989) Strategy and expertise in solving word problems. In: C.B. McCormick, G. Miller & M. Pressley (eds), Cognitive Strategy Research: From Basic Research to Educational Applications, pp. 269–302. New York: Springer.Google Scholar
  10. Gagné, R.M. (1962) The acquisition of knowledge, Psychological Review 69(4): 355–365.Google Scholar
  11. 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(1): 33–41.Google Scholar
  12. Gerjets, P., Scheiter, K. & Catrambone, R. (in press) Reducing cognitive load and fostering cognitive skill acquisition: Benefits of category-avoiding examples. In: R. Alterman & D. Kirsh (eds), Proceedings of the 25th Annual Conference of the Cognitive Science Society. Mahwah, NJ: Erlbaum.Google Scholar
  13. Gerjets, P., Scheiter, K. & Kleinbeck, S. (in press) Instructional examples in hypertext-based learning and problem solving: Comparing transformational and derivational approaches to example design. In: H.M. Niegemann, R. Brünken & D. Leutner (eds), Instructional Design for Multimedia Learning. Muenster: Waxmann.Google Scholar
  14. Gerjets, P., Scheiter, K. & Tack, W.H. (2000) Resource-adaptive selection of strategies in learning from worked-out examples. In: L.R. Gleitman & A.K. Joshi (eds), Proceedings from the 22nd Annual Conference from the Cognitive Science Society, pp. 166–171. Mahwah, NJ: Erlbaum.Google Scholar
  15. Gick, M.L. & Holyoak, K.J. (1983) Schema induction and analogical transfer, Cognitive Psychology 15(1): 1–38.Google Scholar
  16. Hart, S.G. & Staveland, L.E. (1988) Development of NASA-TLX (Task Load Index): Results of experimental and theoretical research. In: P.A. Hancock & N. Meshkati (eds), Human Mental Workload, pp. 139–183. Amsterdam: North Holland.Google Scholar
  17. Mayer, R.E. (1981) Frequency norms and structural analysis of algebra story problems into families, categories, and templates, Instructional Science 10: 135–175.Google Scholar
  18. Paas, F.G.W.C. & Van Merriënboer, J.J.G. (1994) Variability of worked examples and transfer of geometrical problem solving skills: A cognitive load approach, Journal of Educational Psychology 86(1): 122–133.Google Scholar
  19. Pirolli, P. & Recker, M. (1994) Learning strategies and transfer in the domain of programming, Cognition and Instruction 12(3): 235–275.Google Scholar
  20. Quilici, J.L. & Mayer, R.E. (1996) Role of examples in how students learn to categorize statistics word problems, Journal of Educational Psychology 88(1): 144–161.Google Scholar
  21. Reed, S.K. (1993) A schema-based theory of transfer. In: D.K. Detterman & R.J. Sternberg (eds), Transfer on Trial: Intelligence, Cognition, and Instruction, pp. 39–67. Norwood, NJ: Ablex.Google Scholar
  22. Reed, S.K. (1999) Word Problems. Mahwah, NJ: Erlbaum.Google Scholar
  23. Reed, S.K., Dempster, A. & Ettinger, M. (1985) Usefulness of analogous solutions for solving algebra word problems, Journal of Experimental Psychology: Learning, Memory, and Cognition 11(1): 106–125.Google Scholar
  24. Renkl, A. (1997) Learning from worked-out examples: A study on individual differences, Cognitive Science 21(1): 1–29.Google Scholar
  25. Renkl, A. (1999) Learning mathematics from worked-out examples: Analyzing and fostering self-explanations, European Journal of Psychology of Education 14(4): 477–488.Google Scholar
  26. Renkl, A. (2002) Learning from worked-out examples: Instructional explanations supplement self-explanations, Learning and Instruction 12(5): 529–556.Google Scholar
  27. Renkl, A. & Atkinson, R.K. (2003) Structuring the transition from example study to problem solving in cognitive skills acquisition: A cognitive load perspective, Educational Psychologist 38(1): 15–22.Google Scholar
  28. Ross, B.H. (1989) Distinguishing types of superficial similarities: Different effects on the access and use of earlier problems, Journal of Experimental Psychology: Learning, Memory, and Cognition 15(3): 456–468.Google Scholar
  29. Schuh, J., Gerjets, P. & Scheiter, K. (2003) Supporting learning from worked-out examples in computer-based learning environments. In: F. Schmalhofer, R. Young & G. Katz (eds), Proceedings of the European Cognitive Science Conference 2003, pp. 301–306. Mahwah, NJ: Erlbaum.Google Scholar
  30. Sowder, L. (1985) Cognitive psychology and mathematical problem solving: A discussion of Mayer's paper. In: E.A. Silver (ed), Teaching and Learning Mathematical Problem Solving: Multiple Research Perspectives, pp. 139–146. Hillsdale, NJ: Erlbaum.Google Scholar
  31. Sweller, J. (1994) Cognitive load theory, learning difficulty, and instructional design, Learning and Instruction 4(4): 295–312.Google Scholar
  32. Sweller, J. & Cooper, G. (1985) The use of worked examples as a substitute for problem solving in learning algebra, Cognition and Instruction 2(1): 59–89.Google Scholar
  33. Sweller, J., Van Merriënboer, J.J.G. & Paas, F.W.C. (1998) Cognitive architecture and instructional design, Educational Psychology Review 10(3): 251–296.Google Scholar
  34. VanLehn, K. (1989) Problem solving and cognitive skill acquisition. In: M.I. Posner (ed), Foundations of Cognitive Science, pp. 527–579. Cambridge, MA: MIT Press.Google Scholar
  35. VanLehn, K. (1996) Cognitive skill acquisition, Annual Review of Psychology 47: 513–539.Google Scholar
  36. Van Merriënboer, J.J.G., Kirschner, P.A. & Kester, L. (2003) Taking the load of a learner's mind: Instructional design for complex learning, Educational Psychologist 38(1): 5–13.Google Scholar
  37. Van Merriënboer, J.J.G. (1990) Strategies for programming instruction in high school: Program completion vs. program generation, Journal of Educational Computing Research 6(3): 265–287.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Peter Gerjets
    • 1
  • Katharina Scheiter
    • 2
  • Richard Catrambone
    • 3
  1. 1.Knowledge Media Research CenterTuebingenGermany
  2. 2.University of TuebingenTuebingenGermany
  3. 3.Georgia Institute of TechnologyAtlantaUSA

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