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  • Cited by 16
  • Print publication year: 2010
  • Online publication date: June 2012

3 - Schema Acquisition and Sources of Cognitive Load

Summary

INTRODUCTION

The previous chapter outlined the general features of human cognitive architecture relevant to learning. According to this architecture, our schematic knowledge base in long-term memory represents the major critical factor influencing the way we learn new information. In the absence of a relevant knowledge base for a specific situation or task, we apply random search processes to select appropriate actions. Any modifications to our knowledge base for dealing with novel situations occur only under certain restrictive conditions on the amount of such change. Based on these general characteristics of learning within a cognitive load framework, it is possible to formulate general instructional principles that support processes of schema acquisition and enable understanding and learning. This chapter suggests a number of such Cognitive Load Theory (CLT)–generated principles for efficient instruction aimed at acquisition of an organized knowledge base: a direct initial instruction principle, an expertise principle, and a small step-size of change principle. To substantiate these principles, it is necessary first to describe in more detail the concept of schematic knowledge structures and analyze sources of cognitive load that are irrelevant to learning processes.

LEARNING AS SCHEMA ACQUISITION

Schemas represent knowledge as stable patterns of relationships between elements describing some classes of structures that are abstracted from specific instances and used to categorize such instances. Multiple schemas can be linked together and organized into hierarchical structures.

References
Ayres, P., & Sweller, J. (1990). Locus on difficulty in multi-stage mathematics problems. American Journal of Psychology, 103, 167–193.
Baddeley, A. D. (1986). Working memory. New York: Oxford University Press.
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.
Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989). Self-explanation: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145–182.
Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorisation and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.
Chi, M. T. H., & Glaser, R. (1985). Problem solving ability. In Sternberg, R. (Ed.), Human abilities: An information processing approach (pp. 227–250). San Francisco: Freeman.
Chi, M., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In Sternberg, R. (Ed.), Advances in the psychology of human intelligence (pp. 7–75). Hillsdale, NJ: Erlbaum.
Clement, J., & Steinberg, M. (2002). Step-wise evolution of models of electric circuits: A “learning-aloud” case study. Journal of the Learning Sciences, 11, 389–452.
Cooper, G., & Sweller, J. (1987). The effects of schema acquisition and rule automation on mathematical problem-solving transfer. Journal of Educational Psychology, 79, 347–362.
Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining procedures and concepts. Journal of Experimental Psychology: Applied, 7, 68–82.
Groot, A. D. (1966). Perception and memory versus thought: Some old ideas and recent findings. In Kleinmuntz, B. (Ed.), Problem solving: Research, method, and theory (pp. 19–50). New York: Wiley.
diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.
Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149–158.
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.
Glaser, R. (1990). The reemergence of learning theory within instructional research. American Psychologist, 45, 29–39.
Howard, R. W. (1987). Concepts and schemata: Introduction. London: Cassel Educational.
Kalyuga, S. (2005). Prior knowledge principle in multimedia learning. In Mayer, R. (Ed.), Cambridge handbook of multimedia learning (pp. 325–337). New York: Cambridge University Press.
Kalyuga, S. (2006). Rapid cognitive assessment of learners' knowledge structures. Learning & Instruction, 16, 1–11.
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31.
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.
Kalyuga, S., & Sweller, J. (2005). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology, Research and Development, 53, 83–93.
Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17, 248–294.
Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Models of competence in solving physics problems. Cognitive Science, 4, 317–348.
Low, R., & Over, R. (1992). Hierarchical ordering of schematic knowledge relating to area-of-rectangle problems. Journal of Educational Psychology, 84, 62–69.
Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59, 43–64.
Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.
Paas, F., & 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, 122–133.
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.
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, 15–22.
Renkl, A., Atkinson, R. K., & Große, C. S. (2004). How fading worked solution steps works – a cognitive load perspective. Instructional Science, 32, 59–82.
Schneider, W., & Shiffrin, R. (1977). Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review, 84, 1–66.
Shiffrin, R. M., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending and a general theory. Psychological Review, 84, 127–190.
Slotta, J. D., Chi, M. T. H., & Juram, E. (1995). Assessing students' misclassifications of physics concepts: An ontological basis for conceptual change. Cognition and Instruction, 13, 373–400.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.
Sweller, J. (2003). Evolution of human cognitive architecture. In Ross, B. (Ed.), The psychology of learning and motivation (Vol. 43, pp. 215–266). San Diego, CA: Academic Press.
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn? Cognition and Instruction, 12, 185–233.
Sweller, J., Chandler, P., Tierney, P., & Cooper, M. (1990). Cognitive load and selective attention as factors in the structuring of technical material. Journal of Experimental Psychology: General, 119, 176–192.
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition and Instruction, 2, 59–89.
Sweller, J., Merriënboer, J., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
Merriënboer, J. J. G. (1997). Training complex cognitive skills: A four-component instructional design model for technical training. Englewood Cliffs, NJ: Educational Technology Publications.
Merriënboer, J. J. G., Clark, R. E., & Croock, M. B. M. (2002). Blueprints for complex learning: The 4C/ID* model. Educational Technology Research and Development, 50, 39–64.
Merriënboer, J. J. G., Kirschner, P., & Kester, L. (2003). Taking the load off a learner's mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13.