Educational Psychology Review

, Volume 19, Issue 4, pp 509–539

Expertise Reversal Effect and Its Implications for Learner-Tailored Instruction

Review Article

Abstract

The interactions between levels of learner prior knowledge and effectiveness of different instructional techniques and procedures have been intensively investigated within a cognitive load framework since mid-90s. This line of research has become known as the expertise reversal effect. Apart from their cognitive load theory-based prediction and explanation, patterns of empirical findings on the effect fit well those in studies of Aptitude Treatment Interactions (ATI) that were originally initiated in mid-60s. This paper reviews recent empirical findings associated with the expertise reversal effect, their interpretation within cognitive load theory, relations to ATI studies, implications for the design of learner-tailored instructional systems, and some recent experimental attempts of implementing these findings into realistic adaptive learning environments.

Keywords

Expertise reversal effect Prior knowledge Expertise Cognitive load theory Learner-tailored instruction 

References

  1. Anderson, J. R., Corbett, A. T., Fincham, J. M., Hoffman, D., & Pelletier, R. (1992). General principles for an intelligent tutoring architecture. In V. Shute and W. Regian (Eds.), Cognitive approaches to automated instruction (pp. 81–106). Hillsdale, NJ: Erlbaum.Google Scholar
  2. Baddeley, A. D. (1986). Working memory. New York: Oxford University Press.Google Scholar
  3. Bell, B. S., & Kozlowski, S. W. J. (2002). Adaptive guidance: Enhancing self-regulation, knowledge, and performance in technology-based training. Personnel Psychology, 55, 267–306.Google Scholar
  4. Blessing, S. B., & Anderson, J. R. (1996). How people learn to skip steps. Journal of Experimental Psychology: Learning, Memory, and Cognition, 22, 576–598.Google Scholar
  5. Boutwell, R., & Barton, G. (1974). Toward an adaptive learner-controlled model of instruction: A place for the new cognitive aptitudes. Educational Technology, 14, 13–18.Google Scholar
  6. Bracht, G. (1970). Experimental factors related to aptitude–treatment interactions. Review of Educational Research, 40, 627–645.Google Scholar
  7. Calisir, F., & Gurel, Z. (2003). Influence of text structure and prior knowledge of the learner on reading comprehension, browsing and perceived control. Computers in Human Behavior, 19, 135–145.Google Scholar
  8. Camp, G., Paas, F., Rikers, R., & van 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, 575–595.Google Scholar
  9. Chandler, P., & Sweller, J. (1991). Cognitive load theory and the format of instruction. Cognition and Instruction, 8, 293–332.Google Scholar
  10. Chung, J., & Reigeluth, C. M. (1992). Instructional prescriptions for learner control. Educational Technology, 32, 14–20.Google Scholar
  11. 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.Google Scholar
  12. 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.Google Scholar
  13. Cooper, G., Tindall-Ford, S., Chandler, P., & Sweller, J. (2001). Learning by imagining procedures and concepts. Journal of Experimental Psychology Applied, 7, 68–82.PubMedGoogle Scholar
  14. Corbalan, G., Kester, L., & van Merriënboer, J. J. G. (2006). Towards a personalized task selection model with shared instructional control. Instructional Science, 34, 399–422.Google Scholar
  15. Cowan, N. (2001). The magical number 4 in short-term memory: A reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–114.PubMedGoogle Scholar
  16. Cronbach, L. (1967). How can instruction be adapted to individual differences. In R. Gagne (Ed.), Learning and individual differences (pp. 23–39). Columbus, OH: Merrill.Google Scholar
  17. Cronbach, L., & Snow, R. (1969). Individual differences in learning ability as a function of instructional variables (Final Report). Stanford, CA: School of Education, Stanford University.Google Scholar
  18. Cronbach, L., & Snow, R. (1977). Aptitudes and instructional methods: A handbook for research on interactions. New York: Irvington.Google Scholar
  19. De Bra, P., & Calvi, L. (1998). AHA! An open Adaptive Hypermedia Architecture. New Review of Hypermedia and Multimedia, 4, 115–139.Google Scholar
  20. DeStefano, D., & LeFevre, J-A. (2007). Cognitive load in hypertext reading: A review. Computers in Human Behavior, 23, 1616–1641.Google Scholar
  21. Ericsson, K. A., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211–245.PubMedGoogle Scholar
  22. Ericsson, K. A., Krampe, R. T., & Tesch-Romer, C. (1993). The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100, 363–406.Google Scholar
  23. Eysenck, (1982). Attention and arousal: Cognition and performance. Berlin: Springer-Verlag.Google Scholar
  24. Federico, P-A. (1980). Adaptive instruction: Trends and issues. In R. Snow, P-A. Federico and W. Montague (Eds.), Aptitude, learning, and instruction: Vol. 1, Cognitive process analyses of aptitude (pp. 1–26). Hillsdale, NJ: Erlbaum.Google Scholar
  25. Federico, P-A. (1999). Hypermedia environments and adaptive instruction. Computers in Human Behavior, 15, 653–692.Google Scholar
  26. Ginns, P., Chandler, P., & Sweller, J. (2003). When imagining information is effective. Contemporary Educational Psychology, 28, 229–251.Google Scholar
  27. Glaser, R. (1977). Adaptive instruction: Individual diversity and learning. New York: Holt, Rinehart, and Winston.Google Scholar
  28. Kalyuga, S. (2005). Prior knowledge principle. In R. Mayer (Ed.), Cambridge handbook of multimedia learning (pp. 325–337). New York: Cambridge University Press.Google Scholar
  29. Kalyuga, S. (2006a). Assessment of learners’ organized knowledge structures in adaptive learning environments. Applied Cognitive Psychology, 20, 333–342.Google Scholar
  30. Kalyuga, S. (2006b). Instructing and testing advanced learners: A cognitive load approach. New York: Nova Science.Google Scholar
  31. Kalyuga, S. (2006c). Rapid assessment of learners’ proficiency: A cognitive load approach. Educational Psychology, 26, 613–627.Google Scholar
  32. Kalyuga, S. (2006d). Rapid cognitive assessment of learners’ knowledge structures. Learning and Instruction, 16, 1–11.Google Scholar
  33. Kalyuga, S. (2007). When less is more in cognitive diagnosis: A rapid assessment method for adaptive learning environments. Journal of Educational Psychology, (in press).Google Scholar
  34. Kalyuga, S. (2007). Relative effectiveness of animated and static diagrams: An effect of learner prior knowledge. Computers in Human Behavior, 23.Google Scholar
  35. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). Expertise reversal effect. Educational Psychologist, 38, 23–31.Google Scholar
  36. Kalyuga, S., Chandler, P., & Sweller, J. (1998). Levels of expertise and instructional design. Human Factors, 40, 1–17.Google Scholar
  37. Kalyuga, S., Chandler, P., & Sweller, J. (2000). Incorporating learner experience into the design of multimedia instruction. Journal of Educational Psychology, 92, 126–136.Google Scholar
  38. Kalyuga, S., Chandler, P., & Sweller, J. (2001a). Learner experience and efficiency of instructional guidance. Educational Psychology, 21, 5–23.Google Scholar
  39. Kalyuga, S., Chandler, P., Tuovinen, J., & Sweller, J. (2001b). When problem solving is superior to studying worked examples. Journal of Educational Psychology, 93, 579–588.Google Scholar
  40. Kalyuga, S., & Sweller, J. (2004). Measuring knowledge to optimize cognitive load factors during instruction. Journal of Educational Psychology, 96, 558–568.Google Scholar
  41. 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.Google Scholar
  42. Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problem hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17, 248–294.Google Scholar
  43. Kozlowski, S. W. J., Toney, R. J., Mullins, M. E., Weissbein, D. A., Brown, K. G., & Bell, B. S. (2001). Developing adaptability: A theory for the design of integrated-embedded training systems. In E. Salas (Ed.), Advances in human performance and cognitive engineering research (Vol. 1) (pp. 59–123). Amsterdam: Elsevier Science.Google Scholar
  44. Lambiotte, J. G., & Dansereau, D. F. (1992). Effects of knowledge maps and prior knowledge on recall of science lecture content. Journal of Experimental Education, 60, 189–201.CrossRefGoogle Scholar
  45. Leahy, W., & Sweller, J. (2005). Interactions among the imagination, expertise reversal, and element interactivity effects. Journal of Experimental Psychology Applied, 11, 266–276.PubMedGoogle Scholar
  46. Lee, H., Plass, J. L., & Homer, B. D. (2006). Optimizing cognitive load for learning from computer-based science simulations. Journal of Educational Psychology, 98, 902–913.Google Scholar
  47. Leutner, D. (1993). Guided discovery learning with computer based simulation games: Effects of adaptive and non adaptive instructional support. Learning and Instruction, 3, 113–132.Google Scholar
  48. Lohman, D. F. (1986). Predicting mathemathanic effects in the teaching of higher-order thinking skills. Educational Psychologist, 21, 191–208.Google Scholar
  49. Mayer, R. E. (1989). Models for understanding. Review of Educational Research, 59, 43–64.Google Scholar
  50. Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions? Educational Psychologist, 32, 1–19.Google Scholar
  51. Mayer, R. E. (2001). Multimedia learning. Cambridge, MA: Cambridge University Press.Google Scholar
  52. Mayer, R., & Gallini, J. (1990). When is an illustration worth ten thousand words? Journal of Educational Psychology, 82, 715–726.Google Scholar
  53. Mayer, R., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology, 93, 187–198.Google Scholar
  54. Mayer, R. E., Steinhoff, K., Bower, G., & Mars, R. (1995). A generative theory of textbook design: Using annotated illustrations to foster meaningful learning of science text. Educational Technology Research and Development, 43, 31–43.Google Scholar
  55. Mayer, R., Stiehl, C., & Greeno, J. (1975). Acquisition of understanding and skill in relation to subjects’ preparation and meaningfulness of instruction. Journal of Educational Psychology, 67, 331–350.Google Scholar
  56. McNamara, D., Kintsch, E., Songer, N. B., & Kintsch, W. (1996). Are good texts always better? Interactions of text coherence, background knowledge, and levels of understanding in learning from text. Cognition and Instruction, 14, 1–43.Google Scholar
  57. Merrill, M. D. (1975). Learner control: Beyond aptitude–treatment interactions. AV Communication Review, 23(2), 217–226.Google Scholar
  58. 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.PubMedGoogle Scholar
  59. Mousavi, S. Y., Low, R., & Sweller, J. (1995). Reducing cognitive load by mixing auditory and visual presentation modes. Journal of Educational Psychology, 87, 319–334.Google Scholar
  60. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  61. Niemec, P., Sikorski, C., & Walberg, H. (1996). Learner-control effects: A review of reviews and a meta-analysis. Journal of Educational Computing Research, 15, 157–174.CrossRefGoogle Scholar
  62. Ollerenshaw, A., Aidman, E., & Kidd, G. (1997). Is an illustration always worth ten thousand words? Effects of prior knowledge, learning style, and multimedia illustrations on text comprehension. International Journal of Instructional Media, 24, 227–238.Google Scholar
  63. Paas, F., Tuovinen, J., Tabbers, H., & van Gerven, P. (2003). Cognitive load measurement as a means to advance cognitive load theory. Educational Psychologist, 38, 63–71.Google Scholar
  64. 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. Educational Technology Research and Development, 53, 25–34.Google Scholar
  65. Paas, F., & van Merriënboer, J. J. G. (1993). The efficiency of instructional conditions: An approach to combine mental-effort and performance measures. Human Factors, 35, 737–743.Google Scholar
  66. Pollock, E., Chandler, P., & Sweller, J. (2002). Assimilating complex information. Learning and Instruction, 12, 61–86.Google Scholar
  67. Potelle, H., & Rouet, J. F. (2003). Effects of content representation and readers’ prior knowledge on the comprehension of hypertext. International Journal of Human-Computer Studies, 58, 327–345.Google Scholar
  68. Reisslein, J. (2005). Learner achievement and attitudes under varying paces of transitioning to independent problem solving. Doctoral dissertation, Arizona State University.Google Scholar
  69. Reisslein, J., Atkinson, R. K., Seeling, P., & Reisslein, M. (2006). Encountering the expertise reversal effect with a computer-based environment on electrical circuit analysis. Learning and Instruction, 16, 92–103.Google Scholar
  70. Renkl, A. (1997). Learning from worked-out examples: A study on individual differences. Cognitive Science, 21, 1–29.Google Scholar
  71. 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.Google Scholar
  72. 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.Google Scholar
  73. Robinson, D. H., & Molina, E. (2002). The relative involvement of visual and auditory working memory when studying adjunct displays. Contemporary Educational Psychology, 27, 118–131.Google Scholar
  74. Salden, R. J. C. M., Paas, F., Broers, N. J., & van 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, 153–172.Google Scholar
  75. Salden, R. J. C. M., Paas, F., & van Merriënboer, J. J. G. (2006). Personalized adaptive task selection in air traffic control: Effects on training efficiency and transfer. Learning and Instruction, 16, 350–362.Google Scholar
  76. Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. Educational Technology Research and Development, 53, 47–58.Google Scholar
  77. Seufert, T. (2003). Supporting coherence formation in learning from multiple representations. Learning and Instruction, 13, 227–237.Google Scholar
  78. Seufert, T., & Brünken, R. (2006). Cognitive load and the format of instructional aids for coherence formation. Applied Cognitive Psychology, 20, 321–331.Google Scholar
  79. Shapiro, A. M. (1999). The relationship between prior knowledge and interactive overviews during hypermedia-aided learning. Journal of Educational Computing Research, 20, 143–167.Google Scholar
  80. Shiffrin, R., & Schneider, W. (1977). Controlled and automatic human information processing: II. Perceptual learning, automatic attending, and a general theory. Psychological Review, 84, 127–190.Google Scholar
  81. Shin, E. C., Schallert, D. L., & Savenye, W. C. (1994). Effects of learner control, advisement, and prior knowledge on young students’ learning in a hypertext environment. Educational Technology Research and Development, 42, 33–46.Google Scholar
  82. Shute, V. J., & Gluck, K. A. (1996). Individual differences in patterns of spontaneous online tool use. Journal of Learning Sciences, 5, 329–355.Google Scholar
  83. Simon, H. A. (1967). Motivational and emotional controls of cognition. Psychological Review, 74, 29–39.PubMedGoogle Scholar
  84. Snow, R. E. (1989). Aptitude–treatment interaction as a framework for research on individual differences in learning. In P. L. Ackerman, R. J. Sternberg and R. Glaser (Eds.), Learning and individual differences. Advances in theory and research (pp. 13–59). New York: W. H. Freeman.Google Scholar
  85. Snow, R. (1994). Abilities in academic tasks. In R. Sternberg and R. Wagner (Eds.), Mind in context: Interactionist perspectives on human intelligence (pp. 3–37). Cambridge, MA: Cambridge University Press.Google Scholar
  86. Snow, R., & Lohman, D. (1984). Toward a theory of cognitive aptitude for learning from instruction. Journal of Educational Psychology, 76, 347–376.Google Scholar
  87. Steinberg, E. R. (1977). Review of student control in computer-assisted instruction. Journal of Computer-Based Instruction, 3, 84–90.Google Scholar
  88. Steinberg, E. R. (1989). Cognition and learner control: A literature review, 1977–1988. Journal of Computer-Based Instruction, 16, 117–121.Google Scholar
  89. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285.Google Scholar
  90. Sweller, J. (2003). Evolution of human cognitive architecture. In B. Ross (Ed.), The psychology of learning and motivation, Vol. 43 (pp. 215–266). San Diego, CA: Academic Press.Google Scholar
  91. Sweller, J. (2004). Instructional design consequences of an analogy between evolution by natural selection and human cognitive architecture. Instructional Science, 32, 9–31.Google Scholar
  92. Sweller, J. (2007). Evolutionary biology and educational psychology. In J. S. Carlson, & J. R. Levin (Eds.), Psychological perspectives on contemporary educational issues. Greenwich, CT: Information Age Publishing (in press).Google Scholar
  93. 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.Google Scholar
  94. Sweller, J., Mawer, R., & Ward, M. (1983). Development of expertise in mathematical problem solving. Journal of Experimental Psychology General, 12, 639–661.Google Scholar
  95. Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10, 251–296.Google Scholar
  96. Tarmizi, R., & Sweller, J. (1988). Guidance during mathematical problem solving. Journal of Educational Psychology, 80, 424–436.Google Scholar
  97. Tennyson, R. (1975). Adaptive instructional models for concept acquisition. Educational Technology, 15, 7–15.Google Scholar
  98. Tennyson, R. D. (1980). Instructional control strategies and content structure as design variables in concept acquisition using computer-based instruction. Journal of Educational Psychology, 72, 525–532.Google Scholar
  99. Tennyson, R. D. (1981). Use of adaptive information for advisement in learning concepts and rules using computer assisted instruction. American Educational Research Journal, 18, 425–438.Google Scholar
  100. Tennyson, R. D., & Rothen, W. (1979). Management of computer-based instruction: Design of an adaptive control strategy. Journal of Computer-Based Instruction, 5, 126–134.Google Scholar
  101. Tindall-Ford, S., Chandler, P., & Sweller, J. (1997). When two sensory modes are better than one. Journal of Experimental Psychology Applied, 3(4), 257–287.Google Scholar
  102. Tobias, S. (1976). Achievement treatment interactions. Review of Educational Research, 46, 61–74.Google Scholar
  103. Tobias, S. (1987). Mandatory text review and interaction with student characteristics. Journal of Educational Psychology, 79, 154–161.Google Scholar
  104. Tobias, S. (1988). Teaching strategic text review by computer and interaction with student characteristics. Computers in Human Behavior, 4, 299–310.Google Scholar
  105. Tobias, S. (1989). Another look at research on the adaptation of instruction to student characteristics. Educational Psychologist, 24, 213–227.Google Scholar
  106. Tuovinen, J., & Sweller, J. (1999). A comparison of cognitive load associated with discovery learning and worked examples. Journal of Educational Psychology, 91, 334–341.Google Scholar
  107. VanLehn, K., Graesser, A. C., Jackson, G. T., Jordan, P., Olney, A., & Rose, C. P. (2007). When are tutorial dialogues more effective than reading? Cognitive Science, 31, 3–62.Google Scholar
  108. Van Merriënboer, J. J. G. (1990). Strategies for programming instruction in high school: Program completion vs. program generation. Journal of Educational Computing Research, 6, 265–287.CrossRefGoogle Scholar
  109. Van Merrienboer, J. J. G., Kirschner, P. A., & Kester, L. (2003). Taking the load off a learners mind: Instructional design principles for complex learning. Educational Psychologist, 38, 5–13.Google Scholar
  110. Van Merrienboer, J. J. G., & Paas, F. G. W. C. (1989). Automation and schema acquisition in learning elementary computer programming: Implications for the design of practice. Computers in Human Behavior, 6, 273–289.Google Scholar
  111. Van Merriënboer, J. J. G., Sluijsmans, D., Corbalan, G., Kalyuga, S., Paas, F., & Tattersall, C. (2006). Performance assessment and learning task selection in environments for complex learning. In D. Clark and J. Elen (Eds.), Advances in Learning and Instruction (pp. 201–220). Amsterdam: Elsevier Science.Google Scholar
  112. Van Merriënboer, J., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17, 147–177.Google Scholar
  113. Yeung, A. S. (1999). Cognitive load and learner expertise: Split attention and redundancy effects in reading comprehension tasks with vocabulary definitions. Journal of Experimental Education, 67, 197–221.CrossRefGoogle Scholar
  114. Yeung, A. S., Jin, P., & Sweller, J. (1998). Cognitive load and learner expertise: Split attention and redundancy effects in reading with explanatory notes. Contemporary Educational Psychology, 23, 1–21.PubMedGoogle Scholar

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© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.School of EducationThe University of New South WalesSydneyAustralia

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