Skip to main content Accessibility help
×
Hostname: page-component-78c5997874-4rdpn Total loading time: 0 Render date: 2024-11-04T21:22:08.940Z Has data issue: false hasContentIssue false

6 - Instructional Control of Cognitive Load in the Design of Complex Learning Environments

Published online by Cambridge University Press:  05 June 2012

Liesbeth Kester
Affiliation:
Open University of the Netherlands
Fred Paas
Affiliation:
Erasmus University Rotterdam
Jeroen J. G. van Merriënboer
Affiliation:
University of Maastricht
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

Recent instructional design theories (e.g., the case method, project-based education, problem-based learning, and competence-based education) tend to focus on authentic learning tasks that are based on real-life experiences as the driving force for complex learning (Merrill, 2002; van Merriënboer & Kirschner, 2001). According to these theories, authentic learning tasks have many solutions, are ecologically valid, cannot be mastered in a single session, and pose a very high load on the learner's cognitive system. Consequently, complex learning has little to do with learning separate skills in isolation, but foremost it deals with learning to coordinate the separate skills that constitute real-life task performance. Thus, in complex learning, the whole is clearly more than the sum of its parts, because it also includes the ability to coordinate the parts. In addition, in complex learning, effective performance relies on the integration of skills, knowledge, and attitudes, where, for instance, complex knowledge structures are underlying problem-solving and reasoning skills and particular attitudes are critical to interpersonal skills or to performing safety procedures. Moreover, complex learning requires differentiation by recognizing qualitative differences among the task characteristics that influence the constituent skills that have to be applied. Figure 6.1 shows an example of a simulated, authentic learning task for novice electricians in vocational education, namely, troubleshooting electrical circuits.

Type
Chapter
Information
Cognitive Load Theory , pp. 109 - 130
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

Albaret, J. M., & Thon, B. (1999). Differential effects of task complexity on contextual interference in a drawing task. Acta Psychologica, 100, 9–24.CrossRefGoogle Scholar
Atkinson, R. K., Derry, S. J., Renkl, A., & Wortham, D. (2000). Learning from examples: Instructional principles from the worked examples research. Review of Educational Research, 70, 181–214.CrossRefGoogle Scholar
Ayres, P. (2006). Impact of reducing intrinsic cognitive load on learning in a mathematical domain. Applied Cognitive Psychology, 20, 287–298.CrossRefGoogle Scholar
Bainbridge, L. (1997). The change in concepts needed to account for human behaviour in complex dynamic tasks. IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, 27, 351–359.CrossRefGoogle Scholar
Balzer, W. K., Doherty, M. E., & O'Connor, R. (1989). Effects of cognitive feedback on performance. Psychological Bulletin, 106, 410–433.CrossRefGoogle Scholar
Clarke, T., Ayres, P., & Sweller, J. (2005). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology, Research and Development, 53, 15–24.CrossRefGoogle Scholar
Dufresne, R. J., Gerace, W. J., Thibodeau-Hardiman, P., & Mestre, J. P. (1992). Constraining novices to perform expertlike problem analyses: Effects on schema acquisition. The Journal of the Learning Sciences, 2, 307–331.CrossRefGoogle Scholar
Eaton, D., & Cottrell, D. (1999). Structured teaching methods enhance skill acquisition but not problem-solving abilities: An evaluation of the ‘silent run through’. Medical Education, 33, 19–23.CrossRefGoogle Scholar
Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., et al. (1997). Analogical reasoning and conceptual change: A case study of Johannes Kepler. Journal of the Learning Sciences, 6, 3–40.CrossRefGoogle Scholar
Gerjets, P., Scheiter, K., & Catrambone, R. (2004). Designing instructional examples to reduce intrinsic cognitive load: Molar versus modular presentation of solution procedures. Instructional Science, 32, 33–58.CrossRefGoogle Scholar
Guadagnoli, M. A., Dornier, L., & Tandy, R. D. (1996). Optimal length for summary knowledge of results: The influence of task-related experience and complexity. Research Quarterly for Exercise and Sport, 67, 239–248.CrossRefGoogle ScholarPubMed
Hebert, E. P., Landin, D., & Solmon, M. A. (1996). Practice schedule effects on the performance and learning of low- and high-skilled students: An applied study. Research Quarterly for Exercise and Sport, 67, 52–58.CrossRefGoogle ScholarPubMed
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
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (2004a). Information presentation and troubleshooting in electrical circuits. International Journal of Science Education, 26(2/6), 239–256.CrossRefGoogle Scholar
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (2004b). Just in time presentation of different types of information in learning statistics. Instructional Science, 32, 233–252.CrossRefGoogle Scholar
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (2006). Just-in-time information presentation: Improving learning and troubleshooting skill. Contemporary Educational Psychology, 31, 167–185.CrossRefGoogle Scholar
Lee, T. D., & Magill, R. A. (1985). Can forgetting facilitate skill acquisition? In Goodman, D., Wilberg, R. B., & Franks, I. M. (Eds.), Differing perspectives on memory, learning and control (pp. 3–22). Amsterdam, The Netherlands: Elsevier North Holland.CrossRefGoogle Scholar
Machin, M. A. (2002). Planning, managing, and optimizing transfer of training. In Kraiger, K. (Ed.), Creating, implementing, and managing effective training and development (pp. 263–301). San Francisco, CA: Jossey-Bass.Google Scholar
Magill, R. A., & Hall, K. G. (1990). A review of the contextual interference effect in motor skill acquisition. Human Movement Science, 9, 241–289.CrossRefGoogle Scholar
Mandler, J. M., & Mandler, G. (1964). Thinking: From association to Gestalt. New York: Wiley.Google Scholar
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.CrossRefGoogle Scholar
Merrill, M. D. (2002). First principles of instruction. Educational Technology, Research and Development, 50, 43–59.CrossRefGoogle Scholar
Paas, F., Camp, G., & Rikers, R. (2001). Instructional compensation for age-related cognitive declines: Effects of goal specificity in maze learning. Journal of Educational Psychology, 93, 181–186.CrossRefGoogle Scholar
Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38, 1–4.CrossRefGoogle Scholar
Paas, F., Tuovinen, J., Tabbers, H., & Gerven, P. W. M. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.CrossRefGoogle Scholar
Paas, F., & 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
Quilici, J. L., & Mayer, R. E. (1996). Role of examples in how students learn to categorize statistics word problems. Journal of Educational Psychology, 88, 144–161.CrossRefGoogle Scholar
Reigeluth, C. M. (1987). Lesson blueprints based on the elaboration theory of instruction. In Reigeluth, C. M. (Ed.), Instructional theories in action: Lessons illustrating selected theories and models (pp. 245–288). Hillsdale, NJ: Erlbaum.Google Scholar
Reigeluth, C. M. (1999). The elaboration theory: Guidance for scope and sequence decisions. In Reigeluth, C. M. (Ed.), Instructional design theories and models. A new paradigm of instruction (1st ed., pp. 425–453). Mahwah, NJ: Erlbaum.Google Scholar
Reigeluth, C. M., Merrill, M. D., Wilson, B. G., & Spiller, R. T. (1980). The elaboration theory of instruction: A model for sequencing and synthesizing instruction. Instructional Science, 9, 195–219.CrossRefGoogle Scholar
Reigeluth, C. M., & Stein, F. S. (1983). The elaboration theory of instruction. In Reigeluth, C. M. (Ed.), Instructional design theories and models: An overview of their current status (pp. 335–381). Hillsdale, NJ: Erlbaum.Google Scholar
Renkl, A. (2002). Worked-out examples: Instructional explanations support learning by self-explanation. Learning and Instruction, 12, 529–556.CrossRefGoogle Scholar
Renkl, A., & Atkinson, R. K. (2003). Structuring the transition from example study to problem solving in cognitive skill acquisition: A cognitive load perspective. Educational Psychologist, 38, 15–22.CrossRefGoogle Scholar
Robins, S., & Mayer, R. E. (1993). Schema training in analogical reasoning. Journal of Educational Psychology, 85, 529–538.CrossRefGoogle Scholar
Ross, B. H., & Kilbane, M. C. (1997). Effects of principle explanation and superficial similarity on analogical mapping in problem solving. Journal of Experimental Psychology – Learning, Memory & Cognition, 23, 427–440.CrossRefGoogle Scholar
Salomon, G., & Perkins, D. N. (1989). Rocky road to transfer: Rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113–142.CrossRefGoogle Scholar
Schank, R. C. (1993/1994). Goal-based scenarios: A radical look at education. Journal of the Learning Sciences, 3, 429–453.CrossRefGoogle Scholar
Schank, R. C., Berman, T. R., & Macpherson, K. A. (1999). Learning by doing. In Reigeluth, C. M. (Ed.), Instructional design theories and models. A new paradigm of instruction (pp. 161–181). Mahwah, NJ: Erlbaum.Google Scholar
Schank, R. C., Fano, A., Bell, B., & Jona, M. (1993/1994). The design of goal-based scenarios. Journal of the Learning Sciences, 3, 305–345.CrossRefGoogle Scholar
Schmidt, R. A. (1991). Motor control and learning: A behavioral emphasis (2nd ed.). Champaign, IL: Human Kinetics.Google Scholar
Schwartz, D. L., Bransford, J. D., & Sears, D. (2005). Efficiency and innovation in transfer. In Mestre, J. (Ed.), Transfer of learning from a modern multidisciplinary perspective. Greenwich, CT: Information Age Publishing.Google Scholar
Schwartz, D. L., Martin, L., & Pfaffman, J. (2005). How mathematics propels the development of physical knowledge. Journal of Cognition and Development, 6(1), 65–88.CrossRefGoogle Scholar
Shea, J. B., & Zimny, S. T. (1983). Context effects in learning movement information. In Magill, R. A. (Ed.), Memory and the control of action (pp. 345–366). Amsterdam, The Netherlands: Elsevier North Holland.CrossRefGoogle Scholar
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.CrossRefGoogle Scholar
Sweller, J., Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.CrossRefGoogle Scholar
Thorndike, E. L., & Woodworth, R. S. (1901). The influence of improvement in one mental function upon the efficiency of other functions. Psychological Review, 8, 247–261.Google Scholar
Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–285.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(2), 39–64.CrossRefGoogle Scholar
Merriënboer, J. J. G., Croock, M. B. M., & Jelsma, O. (1997). The transfer paradox: Effects of contextual interference on retention and transfer performance of a complex cognitive skill. Perceptual and Motor Skills, 84, 784–786.CrossRefGoogle Scholar
Merriënboer, J. J. G., & Kirschner, P. A. (2001). Three worlds of instructional design: State of the art and future directions. Instructional Science, 29, 429–441.CrossRefGoogle Scholar
Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learners' mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13.CrossRefGoogle Scholar
Merriënboer, J. J. G., Schuurman, J. G., Croock, M. B. M., & Paas, F. (2002). Redirecting learners' attention during training: Effects on cognitive load, transfer test performance, and training efficiency. Learning and Instruction, 12, 11–37.CrossRefGoogle Scholar
Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177.CrossRefGoogle Scholar
Patten, J., Chao, C., & Reigeluth, C. M. (1986). A review of strategies for sequencing and synthesizing instruction. Review of Educational Research, 56, 437–471.CrossRefGoogle Scholar
Wulf, G., & Shea, C. H. (2002). Principles derived from the study of simple skills do not generalize to complex skill learning. Psychonomic Bulletin & Review, 9, 185–211.CrossRefGoogle Scholar
Wulf, G., Shea, C. H., & Whitacre, C. A. (1998). Physical guidance benefits in learning a complex motor skill. Journal of Motor Behavior, 30, 367–380.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] 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
×