Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-v2bm5 Total loading time: 0 Render date: 2025-01-27T03:14:54.148Z Has data issue: false hasContentIssue false

2 - Foundations and Opportunities for an Interdisciplinary Science of Learning

Published online by Cambridge University Press:  05 June 2012

By
John D. Bransford ,
Brigid Barron ,
Roy D. Pea ,
Andrew Meltzoff ,
Patricia Kuhl ,
Philip Bell ,
Reed Stevens ,
Daniel L. Schwartz ,
Nancy Vye  and
Byron Reeves
...Show all authors
Edited by
R. Keith Sawyer
John D. Bransford
Affiliation:
University of Washington
Brigid Barron
Affiliation:
Stanford University
Roy D. Pea
Affiliation:
Stanford University
Andrew Meltzoff
Affiliation:
University of Washington
Patricia Kuhl
Affiliation:
University of Washington
Philip Bell
Affiliation:
University of Washington
Reed Stevens
Affiliation:
University of Washington
Daniel L. Schwartz
Affiliation:
Stanford University
Nancy Vye
Affiliation:
University of Washington
Byron Reeves
Affiliation:
Stanford University
Jeremy Roschelle
Affiliation:
SRI International
Nora H. Sabelli
Affiliation:
SRI International
R. Keith Sawyer
Affiliation:
Washington University, St Louis
Get access

Summary

In this chapter, we argue that the learning sciences are poised for a “decade of synergy.” We focus on several key traditions of theory and research with the potential for mutually influencing one another in ways that can transform how we think about the science of learning, as well as how future educators and scientists are trained.

The three major strands of research that we focus on are: (1) implicit learning and the brain, (2) informal learning, and (3) designs for formal learning and beyond. As Figure 2.1A illustrates, these three areas have mainly operated independently, with researchers attempting to apply their thinking and findings directly to education, and with the links between theory and well-grounded implications for practice often proving tenuous at best.

The goal of integrating insights from these strands in order to create a transformative theory of learning is illustrated in Figure 2.1B. Successful efforts to understand and advance human learning require a simultaneous emphasis on informal and formal learning environments, and on the implicit ways in which people learn in whatever situations they find themselves.

We explore examples of research from each of these three strands. We then suggest ways that the learning sciences might draw on these traditions for creating a more robust understanding of learning, which can inform the design of learning environments that allow all students to succeed in the fast changing world of the twenty-first century (e.g., Darling-Hammond & Bransford, 2005; Vaill, 1996).

Type
Chapter
Information
The Cambridge Handbook of the Learning Sciences , pp. 19 - 34
Publisher: Cambridge University Press
Print publication year: 2005

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Ackerman, P. (2003). Cognitive ability and non-ability trait determinants of expertise. Educational Researcher, 32, 15–20.CrossRefGoogle Scholar
Alexander, P. (2003). The development of expertise: The journey from acclimation to proficiency. Educational Researcher, 32, 10–14.CrossRefGoogle Scholar
Anderson, J. R. (1976). Language, memory and thought. Hillsdale, NJ: Erlbaum.Google Scholar
Anderson, R. C., & Pearson, P. D. (1984). A schema-theoretic view of basic processes in reading comprehension. In Pearson, P. D. (Ed.), Handbook of reading research (pp. 255–291). New York: Longman.Google Scholar
Ash, D. (2003) Dialogic inquiry in life science conversations of family groups in museums. Journal of Research in Science Teaching, 40(2), 138–162.CrossRefGoogle Scholar
Barron, B. (2003). When smart groups fail. The Journal of the Learning Sciences, 12, 307–359.CrossRefGoogle Scholar
Barron, B. (2004). Learning ecologies for technological fluency: Gender and experience differences. Journal of Educational Computing Research, 31(1), 1–36.CrossRefGoogle Scholar
Barron, B. (in press). Knowledge building processes across contexts: A learning ecologies perspective. To appear in Human Development.
Becker, H. S. (1953). Becoming a marihuana user. American Journal of Sociology, 59, 235–242.CrossRefGoogle Scholar
Bereiter, C., & Scardamalia, M. (1989). Intentional learning as a goal of instruction. In Resnick, L. B. (Ed.), Knowing, learning, and instruction: Essays in honor of Robert Glaser (pp. 361–392). Hillsdale, NJ: Erlbaum.Google Scholar
Bereiter, C., & Scardamalia, M. (1993). Surpassing ourselves: An inquiry into the nature and implications of expertise. Chicago, IL: Open Court.Google Scholar
Berliner, D. C. (1991). Educational psychology and pedagogical expertise: New findings and new opportunities for thinking about training. Educational Psychologist, 26(2), 145–155.CrossRefGoogle Scholar
Berliner, D. C. (2001). Learning about and learning from expert teachers. International. Journal of Educational Research, 35(5), 463–468.Google Scholar
Berry, D. C. (1997). How implicit is implicit learning? New York: Oxford University Press.CrossRefGoogle Scholar
Black, J., & Bower, G. (1980). Story understanding as problem-solving. Poetics, 9, 223–250.CrossRefGoogle Scholar
Bransford, J. D., & Johnson, M. K. (1972). Contextual prerequisites for understanding: Some investigations of comprehension and recall. Journal of Verbal Learning and Verbal Behavior, 11, 717–726.CrossRefGoogle Scholar
Bransford, J. D., & Schwartz, D. (1999). Rethinking transfer: A simple proposal with multiple implications. In Iran-Nejad, A. & Pearson, P. D. (Eds.), Review of Research in Education (Vol. 24, pp. 61–100). Washington, DC: American Educational Research Association.Google Scholar
Bransford, J., Vye, N., Stevens, R., Kuhl, P., Schwartz, D., Bell, P., Meltzoff, A., Barron, B., Pea, R., Reeves, B., Roschelle, J., & Sabelli, N. (in press). Learning theories and education: Toward a decade of synergy. In Alexander, P. & Winne, P. (Eds.), Handbook of educational psychology, 2nd ed. Mahwah, NJ: Erlbaum.Google Scholar
Brown, A. L., Bransford, J. D., Ferrara, R. A., & Campione, J. C. (1983). Learning, remembering, and understanding. In Flavell, J. H. & Markman, E. M. (Eds.), Handbook of child psychology: Cognitive development (Vol. 3, pp. 77–166). New York, NY: Wiley.Google Scholar
Bruer, J. T. (1997). Education and the brain: A bridge too far. Educational Researcher, 26, 4–16.CrossRefGoogle Scholar
Chandler-Olcott, K., & Mahar, (2003). “Tech-saviness” meets multiliteracies: Exploring adolescent girls' technology-mediated literacy practices. In Reading Research Quarterly, 38, 356–385.CrossRefGoogle Scholar
Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 1, 33–81.Google Scholar
Cheour, M., Ceponiene, R., Lehtokoski, A., Luuk, A., Allik, J., Alho, K.. (1998). Development of language-specific phoneme representations in the infant brain. Nature Neuroscience, 1, 351–353.CrossRefGoogle ScholarPubMed
Chi, M. T. H., Glaser, R., & Farr, M. (1988). The nature of expertise. Hillsdale, NJ: Erlbaum.Google Scholar
Chi, M. T. H., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In Sternberg, R. J. (Ed.), Advances in the psychology of human intelligence, (Vol. 1, pp. 1–75). Hillsdale, NJ: Erlbaum.Google Scholar
Cleeremans, A., Destrebecqz, A., & Boyer, M. (1998). Implicit learning: news from the front. Trends in Cognitive Sciences, 2, 406–416.CrossRefGoogle ScholarPubMed
Cole, M. (1996). Cultural psychology: A once and future discipline. Cambridge, MA: Harvard University Press.Google Scholar
Cole, M., Gay, J., Glick, J., & Sharp, D. (1971). The cultural context of learning and thinking. New York: Basic Books.Google Scholar
Corsaro, W. (1985). Friendship and peer culture in the early years. Norwood, NJ: Ablex.Google Scholar
Crawford, V., Riel, M., & Schlager, M. (2005, April). Characterizing adaptive expertise in biology teachers' reasoning. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
Crowley, K., & Jacobs, M. (2002). Islands of expertise and the development of family scientific literacy. In Leinhardt, G., Crowley, K. & Knutson, K. (Eds.), Learning conversations in museums. Mahwah, NJ: Erlbaum.Google Scholar
Darling-Hammond, L., & Bransford, J. D. (2005). Preparing teachers for a changing world. Washington, DC: The National Academy Press.Google Scholar
Decety, J., Jackson, P. L., Sommerville, J. A.Chaminade, T., & Meltzoff, A. N. (2004). The neural bases of cooperation and competition: an fMRI investigation. NeuroImage, 23, 744–751.CrossRefGoogle Scholar
deGroot, A. D. (1965). Thought and choice in chess. The Hague, the Netherlands: Mouton.Google Scholar
Eckert, P. (1989). Jocks and burnouts: Social identity in the high school. New York: Teachers College Press.Google Scholar
Fortes, M. (1938) Social and psychological aspects of education in Taleland, Africa, Suppl., 11(4), 1–64.Google Scholar
Gee, J. P. (1996). Social linguistics and literacies: Ideology in discourses. Second Edition. London: Taylor & Francis.Google Scholar
Gentner, D., Brem, S., Ferguson, R. W., Markman, A. B., Levidow, B. B., Wolff, P., & Forbus, K. D. (1997). Analogical reasoning and conceptual change: A case study of Johannes Kepler. Journal of the Learning Sciences, 6(1), 3–40.CrossRefGoogle Scholar
Goodsitt, J. V., Morgan, J. L., & Kuhl, P. K. (1993). Perceptual strategies in prelingual speech segmentation. Journal of Child Language, 20, 229–252.CrossRefGoogle ScholarPubMed
Goodwin, C. (1994). Professional vision. American Anthropologist, 96(3), 606–633.CrossRefGoogle Scholar
Goodwin, M. (1997). Children's linguistic and social worlds. Anthropology Newsletter, 38(4), 1, 3–4.Google Scholar
Gopnik, A., Meltzoff, A. N., & Kuhl, P. K. (1999). The scientist in the crib: Minds, brains, and how children learn. New York: William Morrow.Google Scholar
Greenfield, P. M., & Bruner, J. S. (1966). Culture and cognitive growth. International Journal of Psychology, 1, 89–107.CrossRefGoogle Scholar
Greenwald, A. G., Banaji, M. R., Rudman, L. A., Farnham, S. D., Nosek, B. A., & Mellott, D. S. (2002). A unified theory of implicit attitudes, stereotypes, self-esteem, and self-concept. Psychological Review, 109, 3–25.CrossRefGoogle ScholarPubMed
Greenough, W. T., Black, J. E., & Wallace, C. S. (1987). Experience and brain development. Child Development, 58, 539–559.CrossRefGoogle ScholarPubMed
Hanna, E., & Meltzoff, A. N. (1993). Peer imitation by toddlers in laboratory, home, and day-care contexts: Implications for social learning and memory. Developmental Psychology, 29, 701–710.CrossRefGoogle ScholarPubMed
Hargadon, A., & Sutton, R. I. (2000, May/June). Building an innovation factory. Harvard Business Review, pp. 157–166.Google ScholarPubMed
Hartup, W. W. (1996). The company they keep: friendships and their developmental significance. Child Development, 67, pp. 1–13.CrossRefGoogle ScholarPubMed
Hatano, G. (2005, April). Adaptive expertise. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In Stevenson, H., Azuma, H., & Hakuta, K. (Eds.), Child development and education in Japan (pp. 262–272). New York: Freeman.Google Scholar
Hatano, G., & Osuro, Y. (2003). Commentary: Reconceptualizing school learning using insight from expertise research. Educational Researcher, 32, 26–29.CrossRefGoogle Scholar
Holland, D., Lachicotte, W., Skinner, D., & Cain, C. (1998). Identity and agency in cultural worlds. Cambridge, MA: Harvard University Press.Google Scholar
Hull, G., & Schultz, K. (2001). Literacy and learning out of school. Review of Educational Research, 71 (4), 575–611.CrossRefGoogle Scholar
Hutchins, E. (1995). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
Iverson, P., Kuhl, P., Akahane-Yamada, R., Diesch, E., Tohkura, Y., Ketterman, A., & Siebert, C. (2003). A perceptual interference account of acquisition difficulties for non-native phonemes. Cognition, 87, B47–B57.CrossRefGoogle ScholarPubMed
Joiner, M. J. (1996). “Just Girls”: Literacy and allegiance in junior high school. Written Communication, 13, 93–129.Google Scholar
Karmiloff-Smith, A., & Inhelder, B. (1974) If you want to get ahead, get a theory. Cognition, 3 (3), 195–212.CrossRefGoogle Scholar
Kuhl, P. K. (1991). Human adults and human infants show a “perceptual magnet effect” for the prototypes of speech categories, monkeys do not. Perception & Psychophysics, 50, 93–107.CrossRefGoogle ScholarPubMed
Kuhl, P. K. (2004). Early language acquisition: Cracking the speech code. Nature Reviews Neuroscience, 5, 831–843.CrossRefGoogle ScholarPubMed
Kuhl, P. K, Conboy, B. T., Padden, D., Nelson, T., & Pruitt, J. C. (2005). Early speech perception and later language development: Implications for the “critical period.”Language Learning and Development, 1, 237–264.CrossRefGoogle Scholar
Kuhl, P. K., Tsao, F.-M., & Liu, H.-M. (2003). Foreign-language experience in infancy: Effects of short-term exposure and social interaction on phonetic learning. Proceedings of the National Academy of Sciences, 100, 9096–9101.CrossRefGoogle ScholarPubMed
Kuhl, P. K., Williams, K. A., Lacerda, F., Stevens, K. N., & Lindblom, B. (1992). Linguistic experience alters phonetic perception in infants by 6 months of age. Science, 255, 606–608.CrossRefGoogle ScholarPubMed
Lajoie, S. (2003). Transitions and trajectories for studies of expertise. Educational Researcher, 32, 21–25.CrossRefGoogle Scholar
Lave, J. (1988). Cognition in practice: Mind, mathematics, and culture in everyday life. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Linn, M. C., Davis, E. A., & Bell, P. (2004). Internet environments for science education. Mahwah, NJ: Erlbaum.Google Scholar
Luria, A. R. (1971). Towards the problem of the historical nature of psychological processes. International Journal of Psychology, 6, 259–272.CrossRefGoogle Scholar
Martin, T. (2005, April). Measuring preparation for future learning in children's mathematics: Instructional implications. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
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.CrossRefGoogle ScholarPubMed
McLaughlin, M., Irby, M. A., & Langman, J. (2001). Urban sanctuaries: neighborhood organizations in the lives and futures of inner-city youth. San Francisco: Jossey-Bass.Google Scholar
Mead, M. (1964). Continuities in cultural evolution. New Haven, CT: Yale University Press.Google Scholar
Meltzoff, A. N. (1988a). Imitation, objects, tools, and the rudiments of language in human ontogeny. Human Evolution, 3, 45–64.CrossRefGoogle Scholar
Meltzoff, A. N. (1988b). Imitation of televised models by infants. Child Development, 59, 1221–1229.CrossRefGoogle Scholar
Meltzoff, A. N. (2005). Imitation and other minds: The “like me” hypothesis. In Hurley, S. & Chater, N. (Eds.), Perspectives on imitation: From neuroscience to social science (Vol. 2, pp. 55–77). Cambridge, MA: MIT Press.Google Scholar
Meltzoff, A. N., & Decety, J. (2003). What imitation tells us about social cognition: A rapprochement between developmental psychology and cognitive neuroscience. Philosophical Transactions of the Royal Society of London, Biological Sciences, 358, 491–500.CrossRefGoogle ScholarPubMed
Meltzoff, A. N., & Moore, M. K. (1977). Imitation of facial and manual gestures by human neonates. Science, 198, 75–78.CrossRefGoogle ScholarPubMed
Michael, A. L., Klee, T., Bransford, J. D., & Warren, S. (1993). The transition from theory to therapy: Test of two instructional methods. Applied Cognitive Psychology, 7, 139–154.CrossRefGoogle Scholar
Minsky, M. (1986). Society of mind. New York: Simon & Schuster.Google Scholar
Musen, G., & Triesman, A. (1990). Implicit and explicit memory for visual patterns. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16, 127–137.Google ScholarPubMed
Nasir, N., & Saxe, G. (2003). Ethnic and academic identities: A cultural practice perspective on emerging tensions and their management in the lives of minority students. Educational Researcher, 32 (5), 14–18.CrossRefGoogle Scholar
Näätänen, R., Lehtokoski, A., Lennes, M., Cheour, M., Huotilainen, M., Iivonen, A., Vainio, M., Alku, P., Ilmoniemi, R. J., Luuk, A., Allik, J., Sinkkonen, J., & Alho, K. (1997). Language-specific phoneme representations revealed by electric and magnetic brain responses. Nature, 385, 432–434.CrossRefGoogle ScholarPubMed
Nissen, M. J., & Bullemer, P. T. (1987). Attentional requirements for learning: Evidence from performance measures. Cognitive Psychology, 19, 1–32.CrossRefGoogle Scholar
NRC/National Research Council (2000a). How people learn: Brain, mind, experience, and school (Expanded Edition). Washington, DC: National Academy Press.
NRC/National Research Council (2000b). Neurons to neighborhoods. Washington, DC: National Academy Press.
NRC/National Research Council (2003). Learning and instruction: A SERP research agenda. Washington, DC: National Academy Press.
NRC/National Research Council (2005). How students learn: History, math, and science in the classroom. Washington, DC: National Academies Press.
Pea, R. D. (1993). Practices of distributed intelligence and designs for education. In Salomon, G. (Ed.). Distributed cognitions. New York: Cambridge University Press, pp. 47–87.Google Scholar
Pea, R. D. (2004). The social and technological dimensions of “scaffolding” and related theoretical concepts for learning, education and human activity. The Journal of the Learning Sciences, 13(3), 423–451.CrossRefGoogle Scholar
Petrosino, A. (2005, April). Measures of adaptive expertise in bioengineering. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
Povinelli, D. J., Reaux, J. E., Theall, L. A., & Giambrone, S. (2000). Folk physics for apes: The chimpanzee's theory of how the world works. New York: Oxford University Press.Google Scholar
Reber, A. S. (1967). Implicit learning of artifical grammars. Journal of Verbal Learning and Verbal Behavior, 6, 855–863.CrossRefGoogle Scholar
Reber, A. S. (1976). Implicit learning of synthetic languages: The role of instructional set. Journal of Experimental Psychology: Human Learning and Memory, 2, 88–94.Google Scholar
Reber, A. S. (1993). Implicit learning and tacit knowledge: An essay on the cognitive unconscious. New York: Oxford University Press.Google Scholar
Resnick, L. (1987). Learning in school and out. Educational Researcher, 16(9), 3–21.CrossRefGoogle Scholar
Rideout, V. J., Vandewater, E., & Wartella, E. A. (2003, Fall). Zero to six: Electronic media in the lives of infants, toddlers and preschoolers. Kaiser Family Foundation Report.
Rivera-Gaxiola, M., Silva-Pereyra, J., & Kuhl, P. K. (2005). Brain potentials to native and non-native speech contrasts in 7- and 11-month-old American infants. Developmental Science, 8, 162–172.CrossRefGoogle ScholarPubMed
Rizzolatti, G., Fadiga, L., Fogassi, L., & Gallese, V. (2002). From mirror neurons to imitation, facts, and speculations. In Meltzoff, A. N. & Prinz, W. (Eds.), The imitative mind: Development, evolution, and brain bases (pp. 247–266). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Rogoff, B. (1990). Apprenticeship in thinking: cognitive development in social context. New York: Oxford University Press.Google Scholar
Rogoff, B. (2003). The cultural nature of human development. New York: Oxford University Press.Google Scholar
Rogoff, B., Paradise, R., Mejía Arauz, R., Correa-Chávez, M., & Angelillo, C. (2003). Firsthand learning by intent participation. Annual Review of Psychology, 54, 175–203.CrossRefGoogle ScholarPubMed
Rose, M. (2004). The mind at work: Valuing the intelligence of the American worker. New York: Viking.Google Scholar
Saffran, J. R., Aslin, R. N., & Newport, E. L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926–1928.CrossRefGoogle ScholarPubMed
Salomon, G., & Perkins, D. (1989). Rocky road to transfer: Rethinking mechanisms of a neglected phenomenon. Educational Psychologist, 24, 113–142.CrossRefGoogle Scholar
Schank, R., & Abelson, R. (1977). Scripts, plans, goals and understanding: An inquiry into human knowledge structures. Hillsdale, NJ: Erlbaum Associates.Google Scholar
Schauble, L., Leinhardt, G., & Martin, L. (1998). Organizing a cumulative research agenda in informal learning contexts. Journal of Museum Education, 22(2 & 3), 3–7.CrossRefGoogle Scholar
Scribner, S., & Cole, M. (1973, 9 November). Cognitive consequences of formal and informal education. Science, 182, No. 4112, pp. 553–559.CrossRefGoogle ScholarPubMed
Schwartz, D., Blair, K., Davis, J., Chang, J., & Hartman, K. (2005, April). Iterative dynamic assessments with feedback to students. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
Schwartz, D. L., & Bransford, J. D. (1998). A time for telling. Cognition & Instruction, 16(4), 475–522.CrossRefGoogle Scholar
Schwartz, D., Bransford, J., & Sears, D. (2005). Efficiency and innovation in transfer. In Mestre, J. (Ed.), Transfer of learning: Research and perspectives (pp. 1–52). Greenwich, CT: Information Age Publishing.Google Scholar
Schwartz, D. L., & Martin, T. (2004) Inventing to prepare for learning: The hidden efficiency of original student production in statistics instruction. Cognition & Instruction, 22, 129–184.CrossRefGoogle Scholar
Simon, H. (1996). The sciences of the artificial, 3rd ed. Cambridge, MA: MIT Press.Google Scholar
Spiro, R. J., Vispoel, W. L., Schmitz, J., Samarapungavan, A., & Boeger, A. (1987). Knowledge acquisition for application: Cognitive flexibility and transfer in complex content domains. In Britton, B. C. & Glynn, S. (Eds.), Executive control processes in reading (pp. 177–199). Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
Sternberg, R. (2003). What is an “expert student”? Educational Researcher, 32, 5–9.CrossRefGoogle Scholar
Stevens, R., & Hall, R. (1998). Disciplined perception: Learning to see in technoscience. In Lampert, M. & Blunk, M. L. (Eds.), Talking mathematics in school: Studies of teaching and learning (pp. 107–149). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Strange, W. (1995). Speech perception and linguistic experience: Issues in cross-language research. Timonium, MD: York.Google Scholar
Tomasello, M. (1999). The cultural origins of human cognition. Cambridge, MA: Harvard University Press.Google Scholar
Tomasello, M., & Call, J. (1997). Primate cognition. New York: Oxford University Press.Google Scholar
Vaill, P. B. (1996). Learning as a way of being: Strategies for survival in a world of permanent white water. San Francisco, CA: Jossey-BassGoogle Scholar
Vye, N. J., Schwartz, D. L., Bransford, J. D., Barron, B. J., Zech, L., & Cognition and Technology Group at Vanderbilt. (1998). SMART environments that support monitoring, reflection, and revision. In Hacker, D., Dunlosky, J., & Graesser, A. C. (Eds.), Metacognition in educational theory and practice (pp. 305–346). Mahwah, NJ: Erlbaum.Google Scholar
Walker, J. (2005, April). Design scenarios as a measure of adaptive understanding. Paper presented at the American Educational Research Association Annual Meeting, Montreal, Canada.
Whiten, A. (2002). The imitator's representation of the imitated: Ape and child. In Meltzoff, A. N. & Prinz, W. (Eds.), The imitative mind: Development, evolution, and brain bases (pp. 98–121). Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Wineburg, S. (1998). Reading Abraham Lincoln: An expert/expert study in the interpretation of historical texts. Cognitive Science, 22(3), 319–346.CrossRefGoogle Scholar
Wood, D., Bruner, J., & Ross, G. (1976). The role of tutoring in problem solving. Journal of child psychology and psychiatry, 17, 89–100.CrossRefGoogle ScholarPubMed
Zhang, Y., Kuhl, P. K., Imada, T., Kotani, M., & Tohkura, Y. (2005). Effects of language experience: Neural commitment to language-specific auditory patterns. NeuroImage, 26, 703–720.CrossRefGoogle ScholarPubMed

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×