Skip to main content Accessibility help
×
Home
  • Get access
    Check if you have access via personal or institutional login
  • Cited by 12
  • Print publication year: 2014
  • Online publication date: August 2014

16 - The Worked Examples Principle in Multimedia Learning

from Part III - Advanced Principles of Multimedia Learning

Summary

Extraneous overload occurs when essential cognitive processing (required to understand the essential material in a multimedia message) and extraneous cognitive processing (required to process extraneous material or to overcome confusing layout in a multimedia message) exceed the learner's cognitive capacity. According to the cognitive theory of multimedia learning, the five ways to handle an extraneous overload situation are to: eliminate extraneous material (coherence principle), insert signals emphasizing the essential material (signaling principle), eliminate redundant printed text (redundancy principle), place printed text next to corresponding parts of graphics (spatial contiguity principle), and eliminate the need to hold essential material in working memory for long periods of time (temporal contiguity principle). The research reviewed in this chapter shows that instructional designers should be sensitive to the limitations of working memory by being careful about the amount and layout of information that is presented to learners.

References

Ainsworth, S. E. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning & Instruction, 16, 183–198.
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, 762–773.
Atkinson, R. K., Renkl, A., & Merrill, M. M. (2003). Transitioning from studying examples to solving problems: Combining fading with prompting fosters learning. Journal of Educational Psychology, 95, 774–783.
Baker, R., Walonoski, J., Heffernan, N., Roll, I., Corbett, A., & Koedinger, K. (2008). Why students engage in “gaming the system” behavior in interactive learning environments. Journal of Interactive Learning Research, 19, 185–224.
Bandura, A. (1986). Social foundations of thought and action: A social cognitive. Englewood Cliffs, NJ: Prentice Hall.
Berthold, K., Eysink, T. H., & Renkl, A. (2009). Assisting self-explanation prompts are more effective than open prompts when learning with multiple representations. Instructional Science, 37, 345–363.
Berthold, K., & Renkl, A. (2009). Instructional aides to support a conceptual understanding of multiple representations. Journal of Educational Psychology, 101, 70–87.
Berthold, K., & Renkl, A. (2010). How to foster active processing of explanations in instructional communication. Educational Psychology Review, 22, 25–40.
Booth, J. L., Lange, K. E., Koedinger, K. R., & Newton, K. J. (2013). Using example problems to improve student learning in algebra: Differentiating between correct and incorrect examples. Learning & Instruction, 25, 24–34.
Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: A simple proposal with multiple implications. Review of Research in Education, 24, 61–100.
Catrambone, R. (1996). Generalizing solution procedures learned from examples. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 1020–1031.
Catrambone, R. (1998). The subgoal learning model: Creating better examples so that students can solve novel problems. Journal of Experimental Psychology: General, 127, 355–376.
Chi, M. T. H. (2000). Self-explaining expository texts: The dual process of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology: Educational design and cognitive science (pp. 161–238). Mahwah, NJ: Lawrence Erlbaum.
Conati, C., & VanLehn, K. (2000). Toward computer-based support of meta-cognitive skills: A computational framework to coach self-explanation. International Journal of Artificial Intelligence in Education, 11, 398–415.
Cooper, G., & Sweller, J. (1987). 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.
Cronbach, L. J., Ambron, S. R., Dornbusch, S. M., Hess, R. O., Hornik, R. C., Phillips, D. C., Walker, D. F., & Weiner, S. S. (1980). Toward reform of program evaluation: Aims, methods, and institutional arrangements. San Francisco: Jossey-Bass.
Durkin, K., & Rittle-Johnson, B. (2012). The effectiveness of using incorrect examples to support learning about decimal magnitude. Learning & Instruction, 22, 206–214.
Eysink, T. H. S., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009). Learner performance in multimedia learning arrangements: An analysis across instructional approaches. American Educational Research Journal, 46, 1107–1149.
Gentner, D., Loewenstein, J., & Thompson, L. (2003). Learning and transfer: A general role for analogical encoding. Journal of Educational Psychology, 95, 393–408.
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, 33–41.
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.
Gerjets, P., Scheiter, K., & Catrambone, R. (2006). Can learning from molar and modular worked-out examples be enhanced by providing instructional explanations and prompting self-explanations? Learning & Instruction, 16, 104–121.
Ginns, P., Chandler, P., & Sweller, J. (2003). When imagining information is effective. Contemporary Educational Psychology, 28, 229–251.
Große, C. S., & Renkl, A. (2006). Effects of multiple solution methods in mathematics learning. Learning & Instruction, 16, 122–138.
Große, C. S., & Renkl, A. (2007). Finding and fixing errors in worked examples: Can this foster learning outcomes? Learning & Instruction, 17, 612–634.
Hattie, J. A. C. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. New York: Routledge.
Hilbert, T. S., & Renkl, A. (2009). Learning how to use a computer-based concept-mapping tool: Self-explaining examples helps. Computers in Human Behavior, 25, 267–274.
Hilbert, T. S., Renkl, A., Kessler, S., & Reiss, K. (2008). Learning to prove in geometry: Learning from heuristic examples and how it can be supported. Learning & Instruction, 18, 54–65.
Hilbert, T. S., Renkl, A., Schworm, S., Kessler, S., & Reiss, K. (2008). Learning to teach with worked-out examples: A computer-based learning environment for teachers. Journal of Computer-Assisted Learning, 24, 316–332.
Hilbert, T. S., Schworm, S., & Renkl, A. (2004). Learning from worked-out examples: The transition from instructional explanations to self-explanation prompts. In P. Gerjets, J. Elen, R. Joiner, & P. Kirschner (Eds.), Instructional design for effective and enjoyable computer-supported learning (pp. 184–192). Tübingen: Knowledge Media Research Center.
Holyoak, K. J. (2012). Analogy and relational reasoning. In K. J. Holyoak & R. G. Morrison (Eds.), The Oxford handbook of thinking and reasoning (pp. 234–259). New York: Oxford University Press.
Hübner. S., & Nückles, M., & Renkl, A. (2010). Writing learning journals: Instructional support to overcome learning-strategy deficits. Learning & Instruction, 20, 18–29.
Jeung, H., Chandler, P., & Sweller, J. (1997). The role of visual indicators in dual sensory mode instruction. Educational Psychology, 17, 329–343.
Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.
Kalyuga, S, (2010). Schema acquisition and sources of cognitive load. In J. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 48–64). New York: Cambridge University Press.
Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.
Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.
Koedinger, K. R., & Corbett, A. T. (2006). Cognitive tutors: Technology bringing learning sciences to the classroom. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 61–77). New York: Cambridge University Press.
LeFevre, J.-A., & Dixon, P. (1986). Do written instructions need examples? Cognition & Instruction, 3, 1–30.
Magner, U., Schwonke, R., Aleven, V., Popescu, O., & Renkl, A. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning & Instruction, 29, 141–152.
Mayer, R. E. (2009). Multimedia learning (2d ed). New York: Cambridge University Press.
Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38, 43–52.
Nievelstein, F., van Gog, T., van Dijck, G., & Boshuizen, H. P. A. (2013). The worked example and expertise reversal effect in less structured tasks: Learning to reason about legal cases. Contemporary Educational Psychology, 38, 118–125.
Paas, F., & van Gog, T. (2006). Optimising worked example instruction: Different ways to increase germane cognitive load. Learning & Instruction, 16, 87–91.
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.
Recker, M., & Pirolli, P. (1994). Modeling individual differences in students’ learning strategies. Journal of the Learning Sciences, 4, 1–38.
Reed, S. K., Corbett, A., Hoffman, B., Wagner, A. & McClaren, B. (2013). Effect of worked examples and Cognitive Tutor training on constructing equations. Instructional Science, 41, 1–24.
Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.
Renkl, A. (2002). Learning from worked-out examples: Instructional explanations supplement self-explanations. Learning & Instruction, 12, 529–556.
Renkl, A. (2011). Instruction based on examples. In R. E. Mayer & P. A. Alexander (Eds.), Handbook of research on learning and instruction (pp. 272–295). New York: Routledge.
Renkl, A. (2014). Towards an instructionally-oriented theory of example-based learning. Cognitive Science, 38, 1–37.
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., Maier, U. H., & Staley, R. (2002). From example study to problem solving: Smooth transitions help learning. Journal of Experimental Education, 70, 293–315.
Renkl, A., Berthold, K., Große, C. S., & Schwonke, R. (2013). Making better use of multiple representations: How fostering metacognition can help. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies (pp. 397–408). New York: Springer.
Renkl, A., Gruber, H., Weber, S., Lerche, T., & Schweizer, K. (2003). Cognitive Load beim Lernen aus Lösungsbeispielen [Cognitive load during learning from worked examples]. Zeitschrift für Pädagogische Psychologie, 17, 93–101.
Renkl, A., Hilbert, T., & Schworm, S. (2009). Example-based learning in heuristic domains: A cognitive load theory account. Educational Psychology Review, 21, 67–78.
Renkl, A., & Schwonke, R. (2013). Static visual displays for deeper understanding: How to support learners to make use of them. In G. Schraw, M. McCrudden, & D. Robinson (Eds.), Learning through visual displays (pp. 165–185). Charlotte, NC: Information Age.
Renkl, A., Stark, R., Gruber, H., & Mandl, H. (1998). Learning from worked-out examples: The effects of example variability and elicited self-explanations. Contemporary Educational Psychology, 23, 90–108.
Rittle-Johnson, B., Star, J. R., & Durkin, K. (2009). The importance of prior knowledge when comparing examples: Influences on conceptual and procedural knowledge of equation solving. Journal of Educational Psychology, 101, 836–852.
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.
Rourke, A., & Sweller, J. (2009). The worked-example effect using ill-defined problems: Learning to recognize designers’ styles. Learning & Instruction, 19, 185–199.
Rummel, N., Spada, H., & Hauser, S. (2009). Learning to collaborate while being scripted or by observing a model. International Journal of Computer-Supported Collaborative Learning, 4, 69–92.
Salden, R., Aleven, V., Renkl, A., & Schwonke, R. (2009). Worked examples and tutored problem solving: Redundant or synergistic forms of support? Topics in Cognitive Science, 1, 203–213.
Salden, R., Koedinger, K. R., Renkl, A., Aleven, V., & McLaren, B. M. (2010). Accounting for beneficial effects of worked examples in tutored problem solving. Educational Psychology Review, 22, 379–392.
Seufert, T., & Brünken, R. (2004). Supporting coherence formation in multimedia learning. In P. Gerjets, P. Kirschner, J. Elen, & R. Joiner (Eds.), Instructional design for effective and enjoyable computer-supported learning. Proceedings of the first joint meeting of the EARLI SIGs Instructional Design and Learning and Instruction with Computers (pp. 138–147). Tübingen: Knowledge Media Research Center.
Schwonke, R., Berthold, K., & Renkl, A. (2009). How multiple external representations are used and how they can be made more useful. Applied Cognitive Psychology, 23, 1227–1243.
Schwonke, R., Ertelt, A., Otieno, C., Renkl, A., Aleven, V., & Salden, R. (2013). Metacognitive support promotes an effective use of instructional resources in intelligent tutoring. Learning & Instruction, 23, 136–150.
Schwonke, R., Renkl, A., Krieg, K., Wittwer, J., Aleven, V., & Salden, R. (2009). The worked-example effect: Not an artefact of lousy control conditions. Computers in Human Behavior, 25, 258–266.
Schworm, S., & Renkl, A. (2007). Learning argumentation skills through the use of prompts for self-explaining examples. Journal of Educational Psychology, 99, 285–296.
Schunk, D. H., & Zimmerman, B. J. (2007). Influencing children’s self-efficacy and self-regulation of reading and writing through modeling. Reading & Writing Quarterly, 23, 7–25.
Spanjers, I. A. E., van Gog, T., & van Merriënboer, J. J. G. (2012). Segmentation of worked examples: Effects on cognitive load and learning. Applied Cognitive Psychology, 26, 352–358.
Stark, R. (2004). Implementing example-based learning and teaching in the context of vocational school education in business administration. Learning Environments Research, 7, 134–163.
Sweller, J. (2006). The worked example effect and human cognition. Learning & Instruction, 16, 165–169.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. New York: Springer.
Sweller, J., & Cooper, G. A. (1985). The use of worked examples as a substitute for problem solving in learning algebra. Cognition & Instruction, 2, 59–89.
Sweller, J., van Merriënboer, J. J. G., & Paas, F. G. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.
Tarmizi, R. A., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80, 424–436.
van Gog, T., & Rummel, N. (2010). Example-based learning: Integrating cognitive and social-cognitive research perspectives. Educational Psychology Review, 22, 155–174.
van Loon-Hillen, N., van Gog, T., & Brand-Gruwel, S. (2012). Effects of worked examples in a primary school mathematics curriculum. Interactive Learning Environments, 20, 89–99.
Ward, M., & Sweller, J. (1990). Structuring effective worked examples. Cognition & Instruction, 7, 1–39.
Wittwer, J., & Renkl, A. (2010). How effective are instructional explanations in example-based learning? A meta-analytic review. Educational Psychology Review, 22, 393–409.
Wouters, P., Paas, F., & van Merrienboer, J. J. G. (2009). Observational learning from animated models: Effects of modality and reflection on transfer. Contemporary Educational Psychology, 34, 1–8.
Zhu, X., & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition & Instruction, 4, 137–166.