Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-mcfzb Total loading time: 0 Render date: 2025-03-11T05:13:27.424Z Has data issue: false hasContentIssue false

3 - Schema Acquisition and Sources of Cognitive Load

Published online by Cambridge University Press:  05 June 2012

By
Slava Kalyuga
Edited by
Jan L. Plass ,
Roxana Moreno  and
Roland Brünken
Slava Kalyuga
Affiliation:
University of New South Wales
Jan L. Plass
Affiliation:
New York University
Roxana Moreno
Affiliation:
University of New Mexico
Roland Brünken
Affiliation:
Universität des Saarlandes, Saarbrücken, Germany
Get access

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.

Type
Chapter
Information
Cognitive Load Theory , pp. 48 - 64
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ayres, P., & Sweller, J. (1990). Locus on difficulty in multi-stage mathematics problems. American Journal of Psychology, 103, 167–193.CrossRefGoogle Scholar
Baddeley, A. D. (1986). Working memory. New York: Oxford University Press.Google ScholarPubMed
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorisation and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
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.Google Scholar
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.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining procedures and concepts. Journal of Experimental Psychology: Applied, 7, 68–82.Google Scholar
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.Google Scholar
diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10, 105–225.CrossRefGoogle Scholar
Egan, D. E., & Schwartz, B. J. (1979). Chunking in recall of symbolic drawings. Memory and Cognition, 7, 149–158.CrossRefGoogle ScholarPubMed
Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.CrossRefGoogle ScholarPubMed
Glaser, R. (1990). The reemergence of learning theory within instructional research. American Psychologist, 45, 29–39.CrossRefGoogle Scholar
Howard, R. W. (1987). Concepts and schemata: Introduction. London: Cassel Educational.Google Scholar
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.CrossRefGoogle Scholar
Kalyuga, S. (2006). Rapid cognitive assessment of learners' knowledge structures. Learning & Instruction, 16, 1–11.CrossRefGoogle Scholar
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.CrossRefGoogle Scholar
Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31.CrossRefGoogle Scholar
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17, 248–294.CrossRefGoogle Scholar
Larkin, J., McDermott, J., Simon, D., & Simon, H. (1980). Models of competence in solving physics problems. Cognitive Science, 4, 317–348.CrossRefGoogle Scholar
Low, R., & Over, R. (1992). Hierarchical ordering of schematic knowledge relating to area-of-rectangle problems. Journal of Educational Psychology, 84, 62–69.CrossRefGoogle Scholar
Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59, 43–64.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Renkl, A., Atkinson, R. K., & Große, C. S. (2004). How fading worked solution steps works – a cognitive load perspective. Instructional Science, 32, 59–82.CrossRefGoogle Scholar
Schneider, W., & Shiffrin, R. (1977). Controlled and automatic human information processing: I. Detection, search and attention. Psychological Review, 84, 1–66.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
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.Google Scholar
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn? Cognition and Instruction, 12, 185–233.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Sweller, J., Merriënboer, J., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.CrossRefGoogle Scholar
Merriënboer, J. J. G. (1997). Training complex cognitive skills: A four-component instructional design model for technical training. Englewood Cliffs, NJ: Educational Technology Publications.Google Scholar
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.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×