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The expertise reversal effect is related to the role of prior knowledge in learning. The basic idea is that instructional design recommendations depend on learner knowledge levels. Specifically, the expertise reversal effect occurs when “designs and techniques that are effective with low-knowledge individuals can lose their effectiveness and even have negative consequences for more proficient learners.” [1] To be clear, the expertise reversal effect involves learners with different levels of prior knowledge, but need not involve experts per se. [2] The more proficient learners are often referred to as learners with higher prior knowledge.[3] [1]

The expertise reversal effect is a specific example of an aptitude by treatment interaction (ATI), which is a more general phenomenon in which learning environments that have positive effects for one type of person have neutral or even negative effects for another type of person. [4] The primary recommendation that stems from the expertise reversal effect is that instructional methods often need to be adjusted as learners acquire more knowledge in a specific domain. Importantly, expertise reversal effects often occur when instructional methods differ in the amount of guidance provided. For example, Slava Kalyuga, one of the leading researchers in this area, writes, “instructional guidance, which may be essential for novices, may have negative consequences for more experienced learners.” [3] Thus, for low-knowledge learners well-guided instruction often results in better performance than reduced guidance. But, for higher-knowledge learners, the reverse is true, such that reduced guidance often results in better performance than well-guided instruction. [1] [3]

The Expertise Reversal Effect and Cognitive Load TheoryEdit

The expertise reversal effect is typically explained within a cognitive load framework.[5] [3]Cognitive load theory assumes that a learner’s existing cognitive resources can influence the effectiveness of instructional techniques.[6] The goal of any learning task is to construct integrated mental representations of the relevant information, which requires considerable working memory resources. To accomplish the task without overwhelming working memory, some form of guidance is needed.

Low-knowledge learners lack schema-based knowledge in the target domain and so this guidance comes from instructional supports, which help reduce the cognitive load associated with novel tasks. If the instruction fails to provide guidance, low-knowledge learners often resort to inefficient problem-solving strategies that overwhelm working memory and increase cognitive load. Thus, low-knowledge learners benefit more from well-guided instruction than from reduced guidance.[1]

In contrast, higher-knowledge learners enter the situation with schema-based knowledge, which provides internal guidance. If additional instructional guidance is provided it can result in the processing of redundant information and increased cognitive load. “Learners would have to relate and reconcile the related components of available long-term memory base and externally provided guidance. Such integration processes may impose an additional working memory load and reduce resources available for learning new knowledge.” [1] In this case, the external guidance becomes redundant relative to the learner’s internal schemas and is less beneficial than a reduced-guidance technique.

Although this cognitive load theory-driven explanation for the expertise reversal effect is plausible, there are a few caveats to keep in mind. First, many studies that demonstrate expertise reversal effects rely on subjective measures of cognitive load.[7][8]For example, one common measure is to have learners rate task difficulty by answering the following question on a scale from 1 (extremely easy) to 7 (extremely difficult): “How easy or difficult was it to complete this task?” [9] [10] [11] Some researchers claim that such ratings are increasingly being used as an effective and valid measure of subjective cognitive load. [9] However, others question the use of subjective measures. For example, some question people’s ability to provide accurate self-reports of mental effort. [12] Others suggest that there is no way to know how subjective ratings relate to actual cognitive load. [13] [14] Second, expertise reversal effects have been found in studies outside of the cognitive load paradigm, indicating that alternative explanations remain viable.[1] For example, a number of explanations center on motivational processes.[15] [16]

Examples of Expertise Reversal EffectsEdit

The expertise reversal effect has been found in a variety of domains and for a variety of instructional techniques. Listed below are just a small set of examples, all of which are described more thoroughly in Kalyuga, Ayres, Chandler, & Sweller, 2003. [3]

  1. Interactions between levels of knowledge and the worked example effect: Worked examples provide a problem statement followed by a step-by-step demonstration of how to solve it. Worked examples are often contrasted with open-ended problem solving in which the learner is responsible for providing the step-by-step solution. Low-knowledge learners benefit more from studying structured worked-out examples than from solving problems on their own. However, as knowledge increases, open-ended problem solving becomes the more effective learning activity. [17]

  2. Interactions between levels of knowledge and the imagination effect: The imagination effect occurs when imagining the instructional material is more effective than studying the instructional material. The idea is that imagining the material supports the generation and construction of mental representations. Generally, low-knowledge learners benefit more from studying instructional material than from imagining it. However, as knowledge increases, imagining a procedure or set of relations becomes the more effective learning activity.[18]

  3. Interactions between levels of knowledge and the split attention effect: The split attention effect occurs when two or more related sources of information are presented apart from one another in time or space (e.g., text located separately from a diagram). Mentally integrating the two pieces can require considerable working memory resources. If the sources provide similar information, there are two options to reduce split attention: one is physically to integrate the two sources of the information and the other is simply to eliminate one of them. For low-knowledge learners, physical integration of two or more sources of information is more beneficial than eliminating one of the sources. However, as knowledge increases, eliminating one of the sources becomes the more effective instructional method.[19]


  1. 1.0 1.1 1.2 1.3 1.4 1.5 Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.
  2. Rey, G. D., & Buchwald, F. (2011). The expertise reversal effect: Cognitive load and motivational explanations. Journal of Experimental Psychology: Applied, 17, 33-48.
  3. 3.0 3.1 3.2 3.3 3.4 Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38, 23-31.
  4. Cronbach, L. J., & Snow, R. E. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington
  5. Kalyuga, S. (2009). Knowledge elaboration: A cognitive load perspective. Learning and Instruction, 19, 402-410.
  6. Sweller, J., van Merrienboer, J. J. G., & Paas, F. (1998). Cognitive architecture and instructional media design. Educational Psychology Review, 10, 251-296.
  7. Ayres, P. (2006). Using subjective measures to detect variations of intrinsic cognitive load within problems. Learning and Instruction, 16, 389-400.
  8. Paas, F., Tuovinen, J. E., Tabbers, H., & van Gerven, P. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63-71.
  9. 9.0 9.1 Kalyuga, S., Chandler, P, & Sweller, J. (2004). When redundant on-screen text in multimedia technical instruction can interfere with learning. Human Factors, 46, 567-581.
  10. Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into design of multimedia instruction. Journal of Educational Psychology, 92, 126-136.
  11. 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, 390-397.
  12. Schnotz, W., & Kurschner, C. (2007). A reconsideration of cognitive load theory. Educational Psychology Review, 19, 469-508.
  13. Brunken, R., Plaas J. L., & Leutner, D. (2003). Direct measurement of cognitive load in multimedia learning. Educational Psychologist, 38, 53-61.
  14. Kirschner, P. A., Ayres, P., & Chandler, P. (2011). Contemporary cognitive load research: The good, the bad, and the ugly. Computers in Human Behavior, 27, 99-105.
  15. Paas, F., Tuovinen, J. E., van Merrienboer, J. J. G., & Darabi, A. A. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology Research and Development, 58, 193-198.
  16. Schnotz, W. (2010). Reanalyzing the expertise reversal effect. Instructional Science, 38, 315-323.
  17. 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.
  18. Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining procedures and concepts. Journal of Experimental Psychology: Applied, 7, 68-82.
  19. Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1-17.

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