Educational Psychology Review

, Volume 26, Issue 1, pp 27–50 | Cite as

The Effects of Idealized and Grounded Materials on Learning, Transfer, and Interest: An Organizing Framework for Categorizing External Knowledge Representations

Review Article


Research in both cognitive and educational psychology has explored the effect of different types of external knowledge representations (e.g., manipulatives, graphical/pictorial representations, texts) on a variety of important outcome measures. We place this large and multifaceted research literature into an organizing framework, classifying three categories of external knowledge representations along a dimension of groundedness: (1) idealized, (2) grounded and including only relevant features, and (3) grounded and including irrelevant features. This organizing framework allows us to focus on the implications of these characteristics of external knowledge representations on three important educational outcomes: learning and immediate performance using the target knowledge, the degree to which that knowledge can transfer flexibly, and the interest engendered by the learning materials. We illustrate the framework by mapping a wide body of research from educational and cognitive psychology onto its dimensions. This framework can aid educators by clearly stating what the research literature says about these characteristics of external knowledge representations and how they activate and support the construction of internal knowledge representations. In particular, it will speak to how to best structure instruction using external knowledge representations with different characteristics, depending on the learning objective. Researchers will benefit from the analysis of the current state of knowledge and by the description of what open questions still remain.


Learning materials Idealized Grounded Learning Transfer Interest 


  1. Ainsworth, S. (2006). DeFT: a conceptual framework for considering learning with multiple representations. Learning & Instruction, 16, 183–198.Google Scholar
  2. Alfieri, L., Nokes-Malach, T. J., & Schunn, C. D. (2013). Learning through case comparisons: a meta-analytic review. Educational Psychologist, 48(2), 87–113.Google Scholar
  3. Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of mind. Psychological Review, 111(4), 1036–1060.Google Scholar
  4. Baddeley, A. D. (1986). Working memory. Oxford: Oxford University Press.Google Scholar
  5. Baltes, P. B., Staudinger, U. M., & Lindenberger, U. (1999). Lifespan psychology: theory and application to intellectual functioning. Annual Review of Psychology, 50, 471–507.Google Scholar
  6. Barnett, S. M., & Ceci, S. J. (2002). When and where do we apply what we learn? A taxonomy for far transfer. Psychological Bulletin, 128(4), 612–637.Google Scholar
  7. Bassok, M. (1996). Using content to interpret structure: effects on analogical transfer. Current Directions in Psychological Science, 5(2), 54–57.Google Scholar
  8. Bassok, M., & Holyoak, K. J. (1989). Interdomain transfer between isomorphic topics in algebra and physics. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15(1), 153–166.Google Scholar
  9. Belenky, D. M., & Nokes, T. J. (2009). Examining the role of manipulatives and metacognition on engagement, learning, and transfer. The Journal of Problem Solving, 2(2), 102–129.Google Scholar
  10. Belenky, D. M., & Nokes-Malach, T. J. (2012). Motivation and transfer: the role of mastery-approach goals in preparation for future learning. The Journal of the Learning Sciences, 21(3), 399–432.Google Scholar
  11. Belenky, D. M., & Nokes-Malach, T. J. (2013). Mastery-approach goals and knowledge transfer: an investigation into the effects of task structure and framing instructions. Learning and Individual Differences, 25, 21–34.Google Scholar
  12. Braithwaite, D., & Goldstone, R. L. (2013). Integrating formal and grounded representations in combinatorics learning. Journal of Educational Psychology, 105(3), 666–682.Google Scholar
  13. Bransford, J. D., & Schwartz, D. L. (1999). Rethinking transfer: a simple proposal with multiple implications. Review of Research in Education, 24, 61–100.Google Scholar
  14. Carbonneau, K. J., Marley, S. C., & Selig, J. P. (2013). A meta-analysis of the efficacy of teaching mathematics with concrete manipulatives. Journal of Educational Psychology, 105(2), 380–400.Google Scholar
  15. Carey, S. (2000). Science education as conceptual change. Journal of Applied Developmental Psychology, 21(1), 13–19.Google Scholar
  16. Carey, S. (2009). The origin of concepts. Oxford: Oxford University Press.Google Scholar
  17. Catrambone, R., & Holyoak, K. J. (1989). Overcoming contextual limitations on problem-solving transfer. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15(6), 1147–1156.Google Scholar
  18. Chi, M. T. H. (2005). Commonsense misconceptions of emergent processes: why some misconceptions are robust. The Journal of the Learning Sciences, 14(2), 161–199.Google Scholar
  19. Chi, M. T. H. (2009). Active-constructive-interactive: a conceptual framework for differentiating learning activities. Topics in Cognitive Science, 1, 73–105.Google Scholar
  20. Chi, M. T. H., & VanLehn, K. A. (2012). Seeing deep structure from the interactions of surface features. Educational Psychologist, 47(3), 177–188.Google Scholar
  21. Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.Google Scholar
  22. Cordova, D. I., & Lepper, M. R. (1996). Intrinsic motivation and the process of learning: beneficial effects of contextualization, personalization, and choice. Journal of Educational Psychology, 88(4), 715–730.Google Scholar
  23. Cowan, N. (2000). The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behavioral and Brain Sciences, 24, 87–185.Google Scholar
  24. De Bock, D., Deprez, J., Van Dooren, W., Roelens, M., & Verschaffel, L. (2011). Abstract or concrete examples in learning mathematics? A replication and elaboration of Kaminski, Sloutsky, and Heckler’s study. Journal for Research in Mathematics Education, 42(2), 109–126.Google Scholar
  25. De Corte, E. (2003). Transfer as the productive use of acquired knowledge, skills, and motivations. Current Directions in Psychological Science, 12(4), 142–146.Google Scholar
  26. DeLoache, J. S. (2004). Becoming symbol-minded. Trends in Cognitive Sciences, 8(2), 66–70.Google Scholar
  27. Donaldson, M. (1978). Children’s minds. London: Fontana.Google Scholar
  28. Durik, A. M., & Harackiewicz, J. M. (2007). Different strokes for different folks: how individual interest moderates the effects of situational factors on task interest. Journal of Educational Psychology, 99(3), 597–610.Google Scholar
  29. Dweck, C. (2006). Mindset: the new psychology of success. New York: Random House.Google Scholar
  30. Fyfe, E. R., McNeil, N. M., Son, J. Y., & Goldstone, R. L. (2014/this issue). Concreteness fading offers the best of both concrete and abstract instruction. Educational Psychology Review.Google Scholar
  31. Garner, R., Gillingham, M. G., & White, S. (1989). Effects of ‘seductive details’ on macroprocessing and microprocessing in adults and children. Cognition and Instruction, 6(1), 41–57.Google Scholar
  32. Gentner, D. (2010). Bootstrapping the mind: analogical processes and symbol systems. Cognitive Science, 34, 752–775.Google Scholar
  33. Gick, M. L., & Holyoak, K. J. (1980). Analogical problem solving. Cognitive Psychology, 12, 306–355.Google Scholar
  34. Gick, M. L., & Holyoak, K. J. (1983). Schema induction and analogical transfer. Cognitive Psychology, 15, 1–38.Google Scholar
  35. Goldstone, R. L., & Sakamoto, Y. (2003). The transfer of abstract principles governing complex adaptive systems. Cognitive Psychology, 46(4), 414–466.Google Scholar
  36. Goldstone, R. L., & Son, J. Y. (2005). The transfer of scientific principles using concrete and idealized simulations. The Journal of the Learning Sciences, 14(1), 69–110.Google Scholar
  37. Goldstone, R. L., Landy, D. H., & Son, J. Y. (2010). The education of perception. Topics in Cognitive Science, 2(2), 265–284.Google Scholar
  38. Gopnik, A. (1996). The post-Piaget era. Psychological Science, 7(4), 221–225.Google Scholar
  39. Griggs, R. A., & Cox, J. R. (1982). The elusive thematic-materials effect in Wason’s selection task. British Journal of Psychology, 73(3), 407–420.Google Scholar
  40. Harackiewicz, J. M., Barron, K. E., Tauer, J. M., Carter, S. M., & Elliot, A. J. (2000). Short-term and long-term consequences of achievement goals: predicting interest and performance over time. Journal of Educational Psychology, 92(2), 316–330.Google Scholar
  41. Harackiewicz, J. M., Tauer, J. M., Barron, K. E., & Elliot, A. J. (2002). Predicting success in college: a longitudinal study of achievement goals and ability measures as predictors of interest and performance from freshman year through graduation. Journal of Educational Psychology, 94(3), 562–575.Google Scholar
  42. Harp, S. F., & Maslich, A. A. (2005). The consequences of including seductive details during lecture. Teaching of Psychology, 32(2), 100–103.Google Scholar
  43. 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(3), 414–434.Google Scholar
  44. Hidi, S., & Harackiewicz, J. M. (2000). Motivating the academically unmotivated: a critical issue for the 21st century. Review of Educational Research, 70(2), 151–179.Google Scholar
  45. Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111–127.Google Scholar
  46. Holyoak, K. J. (2005). Analogy. In K. J. Holyoak & R. G. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 117–142). Cambridge: Cambridge University Press.Google Scholar
  47. Holyoak, K. J., & Koh, K. (1987). Surface and structural similarity in analogical transfer. Memory & Cognition, 15(4), 332–340.Google Scholar
  48. Inhelder, B., & Piaget, J. (1958). The growth of logical thinking: from childhood to adolescence. New York: Basic Books.Google Scholar
  49. Johnson-Laird, P. N., Legrenzi, P., & Legrenzi, M. S. (1972). Reasoning and a sense of reality. British Journal of Psychology, 63(3), 395–400.Google Scholar
  50. Kalyuga, S. (2007). Expertise reversal effect and its implications for learner-tailored instruction. Educational Psychology Review, 19, 509–539.Google Scholar
  51. Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.Google Scholar
  52. Kaminski, J. A., & Sloutsky, V. M. (2012). Representation and transfer of abstract mathematical concepts in adolescence and young adulthood. In V. F. Reyna, S. B. Chapman, M. R. Dougherty, & J. Confrey (Eds.), The adolescent brain: learning, reasoning, and decision making (pp. 67–93). Washington, DC: American Psychological Association.Google Scholar
  53. Kaminski, J. A., & Sloutsky, V. M. (2013). Extraneous perceptual information interferes with children’s acquisition of mathematical knowledge. Journal of Educational Psychology, 105(2), 351–363.Google Scholar
  54. Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2008). The advantage of abstract examples in learning math. Science, 320, 454–455.Google Scholar
  55. Kaminski, J. A., Sloutsky, V. M., & Heckler, A. F. (2013). The cost of concreteness: the effect of nonessential information on analogical transfer. Journal of Experimental Psychology. Applied, 19(1), 14–29.Google Scholar
  56. Keil, F. C. (1981). Children’s thinking: what never develops? Cognition, 10, 159–166.Google Scholar
  57. Keil, F. C. (1989). Concepts, kinds and cognitive development. Cambridge: MIT Press.Google Scholar
  58. Kemp, C. (2012). Exploring the conceptual universe. Psychological Review, 119(4), 685–722.Google Scholar
  59. Koedinger, K. R., & Nathan, M. J. (2004). The real story behind story problems: effects of representations on quantitative reasoning. The Journal of the Learning Sciences, 13(2), 129–164.Google Scholar
  60. Koedinger, K. R., Alibali, M. W., & Nathan, M. J. (2008). Trade-offs between grounded and abstract representations: evidence from algebra problem solving. Cognitive Science, 32, 366–397.Google Scholar
  61. Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012). The knowledge-learning-instruction framework: bridging the science–practice chasm to enhance robust student learning. Cognitive Science, 36, 757–798.Google Scholar
  62. Kotovsky, K., Hayes, J. R., & Simon, H. A. (1985). Why are some problems hard? Evidence from Tower of Hanoi. Cognitive Psychology, 17, 248–294.Google Scholar
  63. Langley, P., Laird, J. E., & Rogers, S. (2009). Cognitive architectures: research issues and challenges. Cognitive Systems Research, 10, 141–160.Google Scholar
  64. Lehman, S., Schraw, G., McCrudden, M. T., & Hartley, K. (2007). Processing and recall of seductive details in scientific text. Contemporary Educational Psychology, 32, 569–587.Google Scholar
  65. Magner, U. I. E., Schwonke, R., Aleven, V., & Popescu, O. (2014). Triggering situational interest by decorative illustrations both fosters and hinders learning in computer-based learning environments. Learning & Instruction, 29, 141–152.Google Scholar
  66. Markman, A. B., & Dietrich, E. (2000). In defense of representation. Cognitive Psychology, 40, 138–171.Google Scholar
  67. Marley, S. C., Levin, J. R., & Glenberg, A. B. (2010). What cognitive benefits does an activity-based reading strategy afford young Native American readers? The Journal of Experimental Psychology, 78, 395–417.Google Scholar
  68. Martin, T., & Schwartz, D. L. (2005). Physically distributed learning: adapting and reinterpreting physical environments in the development of fraction concepts. Cognitive Science, 29, 587–625.Google Scholar
  69. Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning. Educational Psychologist, 38(1), 43–52.Google Scholar
  70. Mayer, R. E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: when presenting more material results in less understanding. Journal of Educational Psychology, 93(1), 187–198.Google Scholar
  71. Mayer, R. E., Griffiths, E., Jurkowitz, I. T. N., & Rothman, D. (2008). Increased interestingness of extraneous details in a multimedia science presentation leads to decreased learning. Journal of Experimental Psychology. Applied, 14(4), 329–339.Google Scholar
  72. McDaniel, M. A., Finstad, K., Waddill, P. J., & Bourg, T. (2000). The effects of text-based interest on attention and recall. Journal of Educational Psychology, 92(3), 492–502.Google Scholar
  73. McLaren, I. P. L., Wood, K., & McLaren, R. P. (2013). Naïve physics—the wrong theory? Paper presented at the 35th Annual Meeting of the Cognitive Science Society, Berlin, Germany.Google Scholar
  74. McNeil, N. M., & Fyfe, E. R. (2012). “Concreteness fading” promotes transfer of mathematical knowledge. Learning and Instruction, 22, 440–448.Google Scholar
  75. McNeil, N. M., & Jarvin, L. (2007). When theories don’t add up: disentangling the manipulatives debate. Theory Into Practice, 46, 309–316.Google Scholar
  76. McNeil, N. M., & Uttal, D. H. (2009). Rethinking the use of concrete materials in learning: perspectives from development and education. Child Development Perspectives, 3(3), 137–139.Google Scholar
  77. McNeil, N. M., Uttal, D. H., Jarvin, L., & Sternberg, R. J. (2009). Should you show me the money? Concrete objects both hurt and help performance on mathematics problems. Learning and Instruction, 19, 171–184.Google Scholar
  78. Mevarech, Z., & Stern, E. (1997). Interaction between knowledge and contexts on understanding abstract mathematical concepts. Journal of Experimental Child Psychology, 65, 68–95.Google Scholar
  79. Mitchell, M. (1993). Situational interest: its multifaceted structure in the secondary school mathematics classroom. Journal of Educational Psychology, 85(3), 424–436.Google Scholar
  80. Moreno, R. (2006). Learning in high-tech and multimedia environments. Current Directions in Psychological Science, 15(2), 63–67.Google Scholar
  81. Moreno, R., & Mayer, R. E. (2004). Personalized messages that promote science learning in virtual environments. Journal of Educational Psychology, 96(1), 165–173.Google Scholar
  82. Moreno, R., Ozogul, G., & Reisslein, M. (2011). Teaching with concrete and abstract visual representations: effects on students’ problem solving, problem representations, and learning perceptions. Journal of Educational Psychology, 103(1), 32–47.Google Scholar
  83. Nokes, T. J. (2009). Mechanisms of knowledge transfer. Thinking & Reasoning, 15(1), 1–36.Google Scholar
  84. Nokes, T. J., & Belenky, D. M. (2011). Incorporating motivation into a theoretical framework for knowledge transfer. In J. P. Mestre & B. H. Ross (Eds.), Psychology of learning and motivation: vol. 55. Cognition in education (pp. 109–135). San Diego: Academic.Google Scholar
  85. Nunes, T., Schliemann, A. H., & Carraher, D. W. (1993). Street mathematics and school mathematics. Cambridge: Cambridge University Press.Google Scholar
  86. Paivio, A., Clark, J. M., & Khan, M. (1988). Effects of concreteness and semantic relatedness on composite imagery ratings and cued recall. Memory & Cognition, 16(5), 422–430.Google Scholar
  87. Park, B., Moreno, R., Seufert, T., & Brünken, R. (2011). Does cognitive load moderate the seductive details effect? A multimedia study. Computers in Human Behavior, 27, 5–10.Google Scholar
  88. Petersen, L. A., & McNeil, N. M. (2013). Effects of perceptually rich manipulatives on preschoolers’ counting performance: established knowledge counts. Child Development, 84(3), 1020–1033.Google Scholar
  89. Pugh, K. J., & Bergin, D. A. (2006). Motivational influences on transfer. Educational Psychologist, 41(3), 147–160.Google Scholar
  90. Richland, L. E., Stigler, J. W., & Holyoak, K. J. (2012). Teaching the conceptual structure of mathematics. Educational Psychologist, 47(3), 189–203.Google Scholar
  91. Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W. (2001). Developing conceptual understanding and procedural skill in mathematics: an iterative process. Journal of Educational Psychology, 93(2), 346–362.Google Scholar
  92. Ross, B. H. (1987). This is like that: the use of earlier problems and the separation of similarity effects. Journal of Experimental Psychology. Learning, Memory, and Cognition, 13, 629–639.Google Scholar
  93. Ross, B. H. (1989). Distinguishing types of superficial similarities: different effects on the access and use of earlier problems. Journal of Experimental Psychology. Learning, Memory, and Cognition, 15(3), 456–468.Google Scholar
  94. Sansone, C., & Harackiewicz, J. M. (2000). Intrinsic and extrinsic motivation: the search for optimal motivation and performance. San Diego: Academic.Google Scholar
  95. Schalk, L., Saalbach, H., & Stern, E. (2011). Designing learning materials to foster transfer of principles. Paper presented at the 33rd Annual Conference of the Cognitive Science Society, Austin, TX.Google Scholar
  96. Schiefele, U. (1991). Interest, learning, and motivation. Educational Psychologist, 3 & 4, 299–323.Google Scholar
  97. Schneider, M., Rittle-Johnson, B., & Star, J. R. (2011). Relations among conceptual knowledge, procedural knowledge, and procedural flexibility in two samples differing in prior knowledge. Developmental Psychology, 47(6), 1525–1538.Google Scholar
  98. Schnotz, W. (2005). An integrated model of text and picture comprehension. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (pp. 49–69). Cambridge: Cambridge University Press.Google Scholar
  99. Schweppe, J., & Rummer, R. (2014). Attention, working memory, and long-term memory in multimedia learning: An integrated perspective based on process models of working memory. Educational Psychology Review, in press.Google Scholar
  100. Singley, M. K., & Anderson, J. R. (1989). Transfer of cognitive skill. Cambridge: Harvard University Press.Google Scholar
  101. Sloutsky, V. M., Kaminski, J. A., & Heckler, A. F. (2005). The advantage of simple symbols for learning and transfer. Psychonomic Bulletin & Review, 12(3), 508–513.Google Scholar
  102. Son, J. Y., & Goldstone, R. L. (2009). Contextualization in perspective. Cognition and Instruction, 27(1), 51–89.Google Scholar
  103. Staub, F. C., & Stern, E. (1997). Abstract reasoning with mathematical constructs. International Journal of Educational Research, 27(1), 63–75.Google Scholar
  104. Stenning, K. (2002). Seeing reason: image and language in learning to think. New York: Oxford University Press.Google Scholar
  105. Sweller, J. (1988). Cognitive load during problem solving: effects on learning. Cognitive Science, 12, 257–285.Google Scholar
  106. Sweller, J., van Merrienboer, J. J. G., & Paas, F. G. W. C. (1998). Cognitive architecture and instructional design. Educational Psychology Review, 10(3), 251–296.Google Scholar
  107. Taatgen, N. A. (2013). The nature and transfer of cognitive skills. Psychological Review, 120(3), 439–471.Google Scholar
  108. 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
  109. Uttal, D. H., Scudder, K. V., & DeLoache, J. S. (1997). Manipulatives as symbols: a new perspective on the use of concrete objects to teach mathematics. Journal of Applied Developmental Psychology, 18, 37–54.Google Scholar
  110. Vosniadou, S., & Verschaffel, L. (2004). Extending the conceptual change approach to mathematics learning and teaching. Learning & Instruction, 14(5), 445–451.Google Scholar
  111. Walkington, C. (2013). Using adaptive learning technologies to personalize instruction to student interests: the impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945.Google Scholar
  112. Walkington, C., Petrosino, A., & Sherman, M. (2013). Supporting algebraic reasoning through personalized story scenarios: how situational understanding mediates performance. Mathematical Thinking and Learning, 15(2), 89–120.Google Scholar
  113. Wason, P. C., & Shapiro, D. (1971). Natural and contrived experience in a reasoning problem. Quarterly Journal of Experimental Psychology, 23, 63–71.Google Scholar
  114. Winkielman, P., Schwarz, N., Fazendeiro, T., & Reber, R. (2003). The hedonic marking of processing fluency: implications for evaluative judgment. In J. Musch & K. C. Klauer (Eds.), The psychology of evaluation: affective processes in cognition and emotion (pp. 189–217). Mahwah: Erlbaum.Google Scholar
  115. Yarlas, A. S., & Gelman, R. (1998). Learning as a predictor of situational interest. Paper presented at the Annual Conference of the American Educational Research Association, San Diego, CA, USA.Google Scholar

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© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Human–Computer Interaction InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.ETH ZurichZurichSwitzerland

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