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5 - The Four-Component Instructional Design Model : Multimedia Principles in Environments for Complex Learning

Published online by Cambridge University Press:  05 June 2012

By
Jeroen J. G. van Merriënboer  and
Liesbeth Kester
Edited by
Richard Mayer
Jeroen J. G. van Merriënboer
Affiliation:
Open University of the Netherlands
Liesbeth Kester
Affiliation:
Open University of the Netherlands
Richard Mayer
Affiliation:
University of California, Santa Barbara
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Summary

Abstract

The Four-Component Instructional Design (4C-ID) model claims that four components are necessary to realize complex learning: (1) learning tasks, (2) supportive information, (3) procedural information, and (4) part-task practice. This chapter discusses the use of the model to design multimedia learning environments and relates 14 multimedia principles to each of the four components. Students may work on learning tasks in simulated task environments, where relevant multimedia principles primarily facilitate a process of inductive learning. They may study supportive information in hypermedia systems, where principles facilitate a process of elaboration and mindful abstraction. They may consult procedural information in Electronic Performance Support Systems (EPSSs), where principles facilitate a process of knowledge compilation. Finally, they may be involved in part-task practice with drill and practice Computer-Based Training (CBT) programs, where principles facilitate a process of psychological strengthening. Research implications and limitations of the presented framework are discussed.

Introduction

Theories about learning with multimedia can be positioned at different levels. At a basic level, psychological theories describe memory systems and cognitive processes that explain how people process different types of information and how they learn with different senses. Examples of such theories are Paivio's dual-coding theory (1986; Clark & Paivio, 1991) and Baddeley's working memory model with a central executive and two slave systems, the visuo-spatial sketchpad and the phonological loop (1992; 1997).

Type
Chapter
Information
The Cambridge Handbook of Multimedia Learning , pp. 71 - 94
Publisher: Cambridge University Press
Print publication year: 2005

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References

Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: Learning by doing and explaining with a computer-based cognitive tutor. Cognitive Science, 26, 147–179CrossRefGoogle Scholar
Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence ErlbaumGoogle Scholar
Anderson, J. R., & Lebiere, C. (1998). The atomic components of thought. Mahwah, NJ: Lawrence ErlbaumGoogle 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, 181–214CrossRefGoogle Scholar
Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps. Journal of Educational Psychology, 95, 774–783CrossRefGoogle Scholar
Baddeley, A. D. (1992). Working memory. Science, 255, 556–559CrossRefGoogle ScholarPubMed
Baddeley, A. D. (1997). Human memory: Theory and practice (Rev. ed.). Hove, UK: Psychology PressGoogle Scholar
Bastiaens, Th., Nijhof, W. J., Streumer, J. N., & Abma, H. J. (1997). Working and learning with electronic performance support systems: An effectiveness study. Training for Quality, 5(1), 10–18CrossRefGoogle Scholar
Camp, G., Paas, F., Rikers, R., & Merriënboer, J. J. G. (2001). Dynamic problem selection in air traffic control training: A comparison between performance, mental effort and mental efficiency. Computers in Human Behavior, 17, 575–595CrossRefGoogle Scholar
Carlson, R. A., Khoo, H., & Elliot, R. G. (1990). Component practice and exposure to a problem-solving context. Human Factors, 32, 267–286CrossRefGoogle Scholar
Carlson, R. A., Sullivan, M. A., & Schneider, W. (1989). Component fluency in a problem solving context. Human Factors, 31, 489–502CrossRefGoogle Scholar
Carroll, J. M. (2000). Making use: Scenario-based design of human-computer interactions. Cambridge, MA: MIT PressCrossRefGoogle Scholar
Cerpa, N., Chandler, P., & Sweller, J. (1996). Some conditions under which integrated computer-based training software can facilitate learning. Journal of Educational Computing Research, 15, 345–367CrossRefGoogle Scholar
Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293–332CrossRefGoogle Scholar
Chandler, P., & Sweller, J. (1992). The split attention effect as a factor in the design of instruction. British Journal of Educational Psychology, 62, 233–246CrossRefGoogle Scholar
Chandler, P., & Sweller, J. (1996). Cognitive load while learning to use a computer program. Applied Cognitive Psychology, 10, 151–1703.0.CO;2-U>CrossRefGoogle Scholar
Clark, J. M., & Paivio, A. (1991). Dual coding theory and education. Educational Psychology Review, 3, 149–210CrossRefGoogle Scholar
Clarke, T., Ayres, P., & Sweller, J. (in press). The impact of sequencing and prior knowledge on learning mathematics through spreadsheet applications. Educational Technology, Research and DevelopmentGoogle Scholar
Croock, M. B. M., Merriënboer, J. J. G., & Paas, F. (1998). High versus low contextual interference in simulation-based training of troubleshooting skills: Effects on transfer performance and invested mental effort. Computers in Human Behavior, 14, 249–267CrossRefGoogle 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, 307–331CrossRefGoogle Scholar
Gagné, R. M. (1985). The conditions of learning (4th ed.). New York: Holt, Rinehart and WinstonGoogle Scholar
Gagné, R. M., & Merrill, M. D. (1990). Integrative goals for instructional design. Educational Technology, Research and Development, 38, 23–30CrossRefGoogle 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, 33–58CrossRefGoogle Scholar
Gulikers, J. T. M., Bastiaens, Th. J., & Martens, R. L. (in press). The surplus value of an authentic learning environment. Computers in Human BehaviorGoogle Scholar
Harp, S. F., & Mayer, R. E. (1998). How seductive details do their damage: A theory of cognitive interest in science learning. Journal of Educational Psychology, 90, 414–434CrossRefGoogle Scholar
Holland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (Eds.) (1989). Induction: Processes of inference, learning, and discovery. Cambridge, MA: MIT PressGoogle Scholar
Jeung, H., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17, 329–343CrossRefGoogle Scholar
Kalyuga, S., Ayres, P., & Chandler, P. (2003). The expertise reversal effect. Educational Psychologist, 38, 23–31CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (1999). Managing split-attention and redundancy in multimedia instruction. Applied Cognitive Psychology, 13, 351–3713.0.CO;2-6>CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136CrossRefGoogle Scholar
Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588CrossRefGoogle Scholar
Kalyuga, S., & Sweller, J. (in press). Rapid dynamic assessment of expertise to improve the efficiency of adaptive e-learning. Educational Technology, Research and DevelopmentGoogle Scholar
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (2003). Information presentation and troubleshooting in electrical circuits. International Journal of Science Education, 26, 239–256CrossRefGoogle Scholar
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (in press-a). Timing of information presentation in learning statistics. Instructional ScienceGoogle Scholar
Kester, L., Kirschner, P. A., & Merriënboer, J. J. G. (in press-b). The management of cognitive load during complex cognitive skill acquisition by means of computer simulated problem solving. British Journal of Educational PsychologyGoogle Scholar
Leahy, W., Chandler, P., & Sweller, J. (2003). When auditory presentations should and should not be a component of multimedia instruction. Applied Cognitive Psychology, 17, 401–418CrossRefGoogle Scholar
Leutner, D. (2000). Double-fading support – a training approach to complex software systems. Journal of Computer Assisted Learning, 16, 347–357CrossRefGoogle Scholar
Maran, N. J., & Glavin, R. J. (2003). Low- to high-fidelity simulation: A continuum of medical education?Medical Education, 37(1), 22–28CrossRefGoogle ScholarPubMed
Mautone, P. D., & Mayer, R. E. (2001). Signaling as a cognitive guide in multimedia learning. Journal of Educational Psychology, 93, 377–389CrossRefGoogle Scholar
Mayer, R. E. (2001). Multimedia learning. New York: Cambridge University PressCrossRefGoogle Scholar
Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83, 484–490CrossRefGoogle Scholar
Mayer, R. E., & Anderson, R. B. (1992). The instructive animation: Helping students build connections between words and pictures in multimedia learning. Journal of Educational Psychology, 84, 444–452CrossRefGoogle Scholar
Mayer, R. E., & Chandler, P. (2001). When learning is just a click away: Does simple user interaction foster deeper understanding of multimedia messages?Journal of Educational Psychology, 93, 390–397CrossRefGoogle Scholar
Mayer, R. E., Dow, G. T., & Mayer, S. (2003). Multimedia learning in an interactive self-explaining environment: What works in the design of agent-based microworlds?Journal of Educational Psychology, 95, 806–812CrossRefGoogle Scholar
Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Experimental Psychology, 93, 187–198Google Scholar
Mayer, R. E., Matthias, A., & Wetzell, K. (2002). Fostering understanding of multimedia messages through pre-training: Evidence for a two-stage theory of mental model construction. Journal of Experimental Psychology: Applied, 8, 147–154Google ScholarPubMed
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52CrossRefGoogle Scholar
Mayer, R. E., Moreno, R., Boire, M., & Vagge, S. (1999). Maximizing constructivist learning from multimedia communications by minimizing cognitive load. Journal of Educational Psychology, 91, 638–643CrossRefGoogle Scholar
Mayer, R. E., & Sims, V. K. (1994). For whom is a picture worth a thousand words? Extensions of a dual-coding theory of multimedia learning. Journal of Educational Psychology, 86, 389–401CrossRefGoogle Scholar
Merrill, M. D. (2002). First principles of instruction. Educational Technology, Research and Development, 50, 43–59CrossRefGoogle Scholar
Moreno, R., & Mayer, R. E. (1999). Cognitive principles of multimedia learning: The role of modality and contiguity. Journal of Educational Psychology, 91, 358–368CrossRefGoogle Scholar
Moreno, R., & Mayer, R. E. (2000). A coherence effect in multimedia learning: The case for minimizing irrelevant sounds in the design of multimedia instructional messages. Journal of Experimental Psychology, 94, 117–125Google Scholar
Moreno, R., & Mayer, R. E. (2002). Verbal redundancy in multimedia learning: When reading helps listening. Journal of Educational Psychology, 94, 156–163CrossRefGoogle Scholar
Moreno, R., & Valdez, F. (in press). Cognitive load and learning effects of having students organize pictures and words in multimedia environments: The role of student interactivity and feedback. Educational Technology, Research and DevelopmentGoogle Scholar
Nadolski, R. J., Kirschner, P. A., & Merriënboer, J. J. G. (2001). A model for optimizing step size of learning tasks in competency-based multimedia practicals. Educational Technology, Research and Development, 49, 87–103CrossRefGoogle 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, 122–133CrossRefGoogle Scholar
Paivio, A. (1986). Mental representation: A dual coding approach. New York: Oxford University PressGoogle Scholar
Palmeri, T. J. (1999). Theories of automaticity and the power law of practice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 25, 543–551Google Scholar
Penney, C. (1989). Modality effects and the structure of short-term working memory. Memory and Cognition, 17, 398–422CrossRefGoogle Scholar
Perkins, D. N., & Salomon, G. (1989). Are cognitive skills context-bound?Educational Researcher, 18, 16–25CrossRefGoogle Scholar
Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86CrossRefGoogle 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, 144–161CrossRefGoogle Scholar
Renkl, A. (1999). Learning mathematics from worked-out examples: Analyzing and fostering self-explanations. European Journal of Psychology of Education, 14, 477–488CrossRefGoogle 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, 15–22CrossRefGoogle Scholar
Renkl, A., Atkinson, R. K., & Grosse, C. S. (2004). How fading worked solution steps works – A cognitive load perspective. Instructional Science, 32, 59–82CrossRefGoogle Scholar
Salden, R. J. C. M., Paas, F., Broers, N. J., & Merriënboer, J. J. G. (2004). Mental effort and performance as determinants for the dynamic selection of learning tasks in air traffic control training. Instructional Science, 32, 153–172CrossRefGoogle Scholar
Salden, R. J. C. M., Paas, F., & Merriënboer, J. J. G. (in press). A comparison of approaches to learning task selection in the training of complex cognitive skills. Computers in Human BehaviorGoogle Scholar
Salomon, G. (1998). Novel constructivist learning environments and novel technologies: Some issues to be concerned with. Research Dialogue in Learning and Instruction, 1(1), 3–12CrossRefGoogle Scholar
Straetmans, G., Sluijsmans, D. M. A., Bolhuis, B., & Merriënboer, J. J. G. (2003). Integratie van instructie en assessment in competentiegericht onderwijs [Integration of instruction and assessment in competence based education]. Tijdschrift voor Hoger Onderwijs, 21, 171–197Google Scholar
Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31CrossRefGoogle Scholar
Sweller, J., & Chandler, P. (1994). Why some material is difficult to learn. Cognition and Instruction, 12, 185–233CrossRefGoogle Scholar
Sweller, J., Merriënboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296CrossRefGoogle Scholar
Tabbers, H. K. (2002). The modality of text in multimedia instructions. Refining the design guidelines. Unpublished doctoral dissertation, Open University of the Netherlands, Heerlen, The Netherlands
Tabbers, H. K., Martens, R. L., & van Merriënboer, J. J. G. (2001). The modality effect in multimedia instructions. In Moore, J. D. & Stennings, K. (Eds.), Proceedings of the twenty-third annual conference of the Cognitive Science Society (pp. 1024–1029). Mahwah, NJ: Lawrence ErlbaumGoogle Scholar
Tabbers, H. K., Martens, R. L., & Merriënboer, J. J. G. (in press). Multimedia instructions and cognitive load theory: Effects of modality and cueing. British Journal of Educational PsychologyGoogle Scholar
Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology: Applied, 3, 257–287Google Scholar
Tuovinen, J., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91, 334–341CrossRefGoogle Scholar
Gog, T., Paas, F., & Merriënboer, J. J. G. (2004). Process-oriented worked examples: Improving transfer performance through enhanced understanding. Instructional Science, 32, 83–98Google Scholar
Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–285CrossRefGoogle Scholar
Merriënboer, J. J. G. (1997). Training complex cognitive skills. Englewood Cliffs, NJ: Educational Technology PublicationsGoogle 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, 39–64CrossRefGoogle Scholar
Merriënboer, J. J. G., & Croock, M. B. M. (1992). Strategies for computer-based programming instruction: Program completion vs. program generation. Journal of Educational Computing Research, 8, 365–394CrossRefGoogle Scholar
Merriënboer, J. J. G., Jelsma, O., & Paas, F. (1992). Training for reflective expertise: A four-component instructional design model for complex cognitive skills. Educational Technology Research and Development, 40, 23–43CrossRefGoogle Scholar
Merriënboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load of a learners' mind: Instructional design for complex learning. Educational Psychologist, 38, 5–13CrossRefGoogle Scholar
Van Merriënboer, J. J. G., & Luursema, J. J. (1996). Implementing instructional models in computer-based learning environments: A case study in problem selection. In Liao, T. T. (Ed.), Advanced educational technology: Research issues and future potential (pp. 184–206). Berlin, Germany: Springer VerlagCrossRefGoogle 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, 11–37CrossRefGoogle Scholar
Willoughby, T., Wood, E., Desmarais, S., Sims, S., & Kalra, M. (1997). Mechanisms that facilitate the effectiveness of elaboration strategies. Journal of Educational Psychology, 89, 682–685CrossRefGoogle Scholar

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