Paving a clear path in a thick forest: a conceptual analysis of a metacognitive component

Abstract

The concept of metacognition refers to one’s knowledge and control of one’s own cognitive system. However, despite being widely used, this concept is confusing because of several reasons. First, sometimes it is not at all clear what is cognitive and what is metacognitive. Second, researchers often use the same term, namely, “metacognition” even when they refer to very different aspects of this complex concept. Alternatively, researchers may use different terms to indicate the same metacognitive elements. Another foggy matter is the interrelationships among the various components of metacognition discussed in the literature. This conceptual confusion regarding the concept of metacognition and its sub-components calls for in-depth theoretical and conceptual clarifications. The goal of this article is to portray a detailed example of a conceptual analysis of meta-strategic knowledge (MSK) which is one specific component of metacognition. This specific example is used to draw a general model for conceptual analyses of additional metacognitive components. The approach suggested here is to begin with a clear definition of the target sub component of metacognition, followed by a systematic examination of this sub component according to several dimensions that are relevant to metacognition in general and to that sub component in particular. The examination should include an analysis of how the details of the definition of the target sub-component refer to: (a) general theoretical metacognitive issues raised by prominent scholars; (b) definitions formulated and issues raised by other researchers who have investigated the same (or a similar) sub-component and, (c) empirical findings pertaining to that sub-component. Finally, it should be noted that since metacognition is a relational rather than a definite concept it is important to situate the context within which the conceptual analysis takes place.

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References

  1. Adolph, K. E. (1995). A psychophysical assessment of toddlers’ ability to cope with slopes. Journal of Experimental Psychology. Human Perception and Performance, 21, 734–750. doi:10.1037/0096-1523.21.4.734.

    Article  Google Scholar 

  2. Alibali, M. W. (1999). How children change their minds: Strategy change can be gradual or abrupt. Developmental Psychology, 35, 127–145. doi:10.1037/0012-1649.35.1.127.

    Article  Google Scholar 

  3. Alibali, M. W., & Goldin-Meadow, S. (1993). Transitions in learning: What the hands reveal about a child’s state of mind. Cognitive Psychology, 25, 468–523. doi:10.1006/cogp.1993.1012.

    Article  Google Scholar 

  4. Bell, R. L., Lederman, N. G., & Abd-El- Khalick, F. (2000). Developing and Acting upon one’s conception of the Nature of Science: A follow-up study. Journal of Research in Science Teaching, 37, 563–581. doi:10.1002/1098-2736(200008)37:6<563::AID-TEA4>3.0.CO;2-N.

    Article  Google Scholar 

  5. Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, Remembering and understanding. In: P. H. Mussen (Ed.). Handbook of Child Pshycology. Vol 3 (pp. 77–155): Cognitive Development. Wiley.

  6. Cardelle-Elawar, M. (1995). Effects of teaching metacognitive skills to students with low mathematics ability. Teaching and Teacher Education, 8, 109–121. doi:10.1016/0742-051X(92)90002-K.

    Article  Google Scholar 

  7. Changeux, J. P., & Dehaene, S. (1989). Neuronal models of cognitive functions. Cognition, 33, 63–109. doi:10.1016/0010-0277(89)90006-1.

    Article  Google Scholar 

  8. Chen, Z., & Klahr, D. (1999). All other thing being equal: children’s acquisition of the control of variables strategy. Child Development, 70, 1098–1120. doi:10.1111/1467-8624.00081.

    Article  Google Scholar 

  9. Church, R. B. (1999). Using gesture and speech to capture transitions in learning. Cognitive Development, 14, 313–342. doi:10.1016/S0885-2014(99)00007-6.

    Article  Google Scholar 

  10. Church, R. B., & Goldin-Meadow, S. (1986). The mismatch between gesture and speech as an index of transitional knowledge. Cognitive, 23, 43–71.

    Google Scholar 

  11. Coyle, T. R., & Bjorklund, D. F. (1996). The development of strategic memory: a modified microgenetic assessment of utilization deficiencies. Cognitive Development, 11, 295–314. doi:10.1016/S0885-2014(96)90006-4.

    Article  Google Scholar 

  12. Dean, D., & Kuhn, D. (2007). Direct Instruction vs discovery: the long view. Science Education, 91(3), 384–397. doi:10.1002/sce.20194.

    Article  Google Scholar 

  13. Edelman, G. (1987). Neural Darwinism: the theory of neuronal group selection. New York: Basic Books.

    Google Scholar 

  14. Efklides, A. (2005). Motivation and affect in the self-regulation of behavior. European Psychologist, 10, 173–174. doi:10.1027/1016-9040.10.3.173.

    Article  Google Scholar 

  15. Efklides, A., & Petkaki, C. (2005). Effects of mood on students’ metacognitive experiences. Learning and Instruction, 15, 415–432. doi:10.1016/j.learninstruc.2005.07.010.

    Article  Google Scholar 

  16. Flavell, J. H., Miller, P. H., & Miller, S. A. (2002). Cognitive development (4th ed.). Upper Saddle River, New Jersey: Prentice Hall.

    Google Scholar 

  17. Geary, D. C., & Bjorklund, D. F. (2000). Evolutionary developmental psychology. Child Development, 71, 57–65. doi:10.1111/1467-8624.00118.

    Article  Google Scholar 

  18. Goldin-Meadow, S. (2001). Giving the mind a hand: the role of gesture in cognitive change. In J. L. McClelland & R. S. Siegler (Eds.), Mechanisms of cognitive development: Behavioral and neural perspectives. Mahwah, NJ: Erlbaum.

    Google Scholar 

  19. Goldin-Meadow, S., & Alibali, M. W. (2002). Looking at the hands through time: a microgenetic perspective on learning and instruction. In N. Granott & J. Parziale (Eds.), Microdevelopment: transitions processes in development and learning. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  20. Granott, N. (2002). How microdevelopment creates macrodevelopment: reiterated sequences, backward transitions, and the zone of current development. In N. Granott & J. Parziale (Eds.), Microdevelopment: transitions processes in development and learning. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  21. Johnson, M. H., & Gilmore, R. D. (1996). Developmental cognitive neuroscience: a biological perspective on cognitive change. In R. Gelman & T. Au (Eds.), Handbook of perception and cognitive: Perceptual and cognitive development (Vol. 13). Orlando. FL: Academic Press.

    Google Scholar 

  22. Karmiloff-Smith, A. (1992). Beyond modularity: a developmental perspective on cognitive science. Cambridge, MA: MIT Press.

    Google Scholar 

  23. Klahr, D., & Nigam, M. (2004). The equivalence of learning paths in early science instruction: effects of direct instruction and discovery learning. Psychological Science, 15, 661–667.

    Article  Google Scholar 

  24. Kramarski, B., Mevarech, Z. R., & Arami, M. (2002). The effects of metacognitive instruction on solving mathematical authentic tasks. Educational Studies in Mathematics, 49, 225–250.

    Article  Google Scholar 

  25. Kuhn, D. (1995). Microgenetic study of change: what has it told us? Psychological Science, 6, 133–139.

    Article  Google Scholar 

  26. Kuhn, D. (1999). Metacognitive development. In L. Balter & C. S. Tamis-LeMonda (Eds.), Child psychology, a handbook of contemporary issues. Ann Arbor, MI: Taylor and Francis.

    Google Scholar 

  27. Kuhn, D. (2000a). Metacognitive development. Current Developments in Psychological Science, 9, 178–181.

    Article  Google Scholar 

  28. Kuhn, D. (2000b). Why development does (and doesn’t) occur: evidence from the domain of inductive reasoning. In R. Siegler & J. McClelland (Eds.), Mecahnisms of cognitive development: neural and behavioral perspectives. Mahwah, NJ: Erlbaum.

    Google Scholar 

  29. Kuhn, D. (2001a). How do people know? Psychological Science, 2001, 1–8.

    Article  Google Scholar 

  30. Kuhn, D. (2001b). Theory of mind, metacognition and reasoning: a life-span perspective. In H. Hartman (Ed.), Metacognition in learning and instruction, pp. 301–326. Netherlans: Kluwer.

    Google Scholar 

  31. Kuhn, D., & Pearsall, S. (1998). Relations between metastrategic knowledge and strategic performance. Cognitive Development, 13, 227–247.

    Article  Google Scholar 

  32. Kuhn, D., Schauble, L., & Garcia-Mila, M. (1992). Cross- domain development of scientific reasoning. Cognition and Instruction, 9(4), 285–327.

    Article  Google Scholar 

  33. Kuhn, D., Garcia-Mila, M., Zohar, A., & Anderson, C. (1995). Strategies of knowledge Acquisition. To be printed in: Monographs of the Society for Research in Child Development (MSRCD)

  34. Kuhn, D., Katz, J., & Dean, D. (2004). Developing Reason. Thinking & Reasoning, 10, 197–219.

    Article  Google Scholar 

  35. Lederman, N. G., Abd-El-Khalick, F., Bell, R. L., & Schwartz, R. S. (2002). Views of nature of science questionnaire: toward valid and meaningful assessment of learners’ conception of nature of science. Journal of Research in Science Teaching, 39, 497–521.

    Article  Google Scholar 

  36. Mevarech, Z. R. (1999). Effects of metacognitive training embedded in cooperative settings on mathematical problem solving. Journal of Educational Research, 92(4), 195–205.

    Article  Google Scholar 

  37. Mevarech, Z. R., & Kramarski, B. (1997). IMPROVE: a multidimensional method for reaching mathematics in heterogeneous classrooms. American Educational Research Journal, 34, 365–394.

    Google Scholar 

  38. Mevarech, Z. R., & Fridkin, S. (2006). Who benefits from IMPROVE? The differential effects of IMPROVE on mathematical knowledge and reasoning. School of Education, Bar- Ilan University, Israel. Paper presented at SIG16 Metacognition Conference, Cambridge, UK. July 2006.

  39. Nelson, T. O. (1996). Consciousness and metacognition. American Psychologist, 51, 102–116.

    Article  Google Scholar 

  40. Nelson, T. O., & Narens, L. (1994). Why investigate metacognition? In J. Metcalfe & A. P. Shimamura (Eds.), Metacognition: knowing about knowing, Chapter 1, pp. 1–26. Cambridge, MA: The MIT Press.

    Google Scholar 

  41. Perkins, D. N., & Salomon, G. (1989). Are cognitive skills context-bound? Educational Researcher, 18, 16–25.

    Google Scholar 

  42. Perry, M., & Lewis, J. L. (1999). Verbal imprecision as an index of knowledge in transition. Developmental Psychology, 35, 749–759.

    Article  Google Scholar 

  43. Perry, M., Church, R. B., & Goldin-Meadow, S. (1988). Transitional knowledge in the acquisition of concepts. Cognitive Development, 3, 359–400.

    Article  Google Scholar 

  44. Ross, J. A. (1988). Controlling variables: a meta-analysis of studies. Review of Educational Research, 58(4), 405–437.

    Google Scholar 

  45. Schraw, G. (1998). Promoting general metacognitive awareness. Instructinal Science, 26, 113–125.

    Article  Google Scholar 

  46. Shrager, J., & Siegler, R. S. (1998). SCADS: A model of children’s strategy choices and strategy discoveries. Psychological Science, 9, 405–410.

    Article  Google Scholar 

  47. Siegler, R. S. (1995). How does change occur: a microgenetic study of number conservation. Cognitive Psychology, 28, 225–273.

    Article  Google Scholar 

  48. Siegler, R. S. (1996). Emerging minds: the process of change in children's thinking. New-York and Oxford: Oxford University Press.

    Google Scholar 

  49. Siegler, R. S. (2000). The rebirth of children's learning. Child Development, 71, 26–35.

    Article  Google Scholar 

  50. Siegler, R. S. (2002). Microgenetic studies of self-explanation. In N. Granott & J. Parziale (Eds.), Microdevelopment: transitions processes in development and learning. Cambridge, UK: Cambridge University Press.

    Google Scholar 

  51. Siegler, R. S., & Jenkins, R. (1989). How children discover new strategies. Hillsdale NJ: Erlbaum.

    Google Scholar 

  52. Siegler, R. S., & Crowley, K. (1991). The microgenetic method: a direct means for studying cognitive development. American Psychologist, 46, 606–620.

    Article  Google Scholar 

  53. Siegler, R. S., & Stern, E. (1998). Conscious and unconscious strategy discoveries: a microgenetic analysis. Journal of Experimental Psychology: General, 127, 377–397.

    Article  Google Scholar 

  54. Siegler, R. S., & Svetina, M. (2002). A microgenetic/cross-sectional study of matrix completion: comparing short-term and long-term change. Child Development, 73, 793–8.

    Article  Google Scholar 

  55. Teong, S. K. (2003). The effect of metacognitive training on mathematical word-problem solving. Journal of Computer Assisted Learning, 19(1), 46–55.

    Article  Google Scholar 

  56. Toth, E. E., Klahr, D., & Chen, Z. (2000). Bridging research, practice: a cognitively based classroom intervention for Schraw, G. (1998). Promoting general metacognitive awareness. Instructinal Science, 26, 113–125.

    Google Scholar 

  57. Tunteler, E., & Resing, W. C. M. (2002). Spontaneous analogical transfer in 4-year-olds: A microgenetic study. Journal of Experimental Child Psychology, 83, 149–166.

    Article  Google Scholar 

  58. Veenman, M. V. J. (2006). Metacognition: Definitions, constituents, and their intricate relation with cognition. Symposium organized by Marcel V.J. Veenman, Anat Zohar, and Anastasia Efklides for the 2nd conference of the EARLI SIG on Metacognition (SIG 16), Cambridge, UK, July 19-21.

  59. Veenman, M. V. J., Elshout, J. J., & Busato, V. V. (1994). Metacognitive mediation in learning with computer-based simulations. Computers in Human Behavior, 10, 93–106.

    Article  Google Scholar 

  60. Veenman, M. V. J., Van Hout-Wolters, B., & Afflerbach, P. (2006). Metacognition and learning: conceptual and methodological considerations. Metacognition Learning, 1, 3–14.

    Article  Google Scholar 

  61. White, B. Y., & Frederiksen, J. R. (1998). Inquiry, modeling and metacognition: making science accessible to all students. Cognition and Instruction, 16(1), 3–118.

    Article  Google Scholar 

  62. White, B. Y., & Frederiksen, J. R. (2000). Metacognitive facilitation: an approach to making scientific inquiry accessible to all. In J. L. Minstrell & E. H. Van-Zee (Eds.), Inquiry into Inquiry learning and Teaching in Science, pp. 331–370. Washington D.C: American Association for the Advancement of Science.

    Google Scholar 

  63. Zohar, A. (2006a). The nature and development of teachers’ metastrategic knowledge in the context of teaching higher order thinking. The Journal of Learning Sciences, 15(3), 331–377.

    Article  Google Scholar 

  64. Zohar, A. (2006b). A conceptual analysis of meta strategic knowledge: a specific case and general model. Presented in the Symposium: Metacognition: Definitions, constituents, and their intricate relation with cognition, the 2nd conference of the EARLI SIG on Metacognition (SIG 16), Cambridge, UK, July 19–21.

    Article  Google Scholar 

  65. Zohar, A., & Ben David, A. (2008). Explicit teaching of meta-strategic knowledge in authentic classroom situations. Metacognition and Learning, 3, 59–82.

    Article  Google Scholar 

  66. Zohar, A., & Peled, B. (2008). The effects of explicit teaching of metastrategic knowledge on low- and high-achieving students. Learning and Instruction, 18, 337–353.

    Article  Google Scholar 

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Correspondence to Anat Zohar.

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This study was supported by the Israel Science Foundation grant # 802/03.

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Zohar, A., Ben David, A. Paving a clear path in a thick forest: a conceptual analysis of a metacognitive component. Metacognition Learning 4, 177–195 (2009). https://doi.org/10.1007/s11409-009-9044-6

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Keywords

  • Metacognition
  • Meta strategic knowledge
  • Control of variables
  • Conceptual analysis