Skip to main content Accessibility help
×
Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-19T16:16:04.108Z Has data issue: false hasContentIssue false

4 - Some Early Contributions to the Situative Perspective on Learning and Cognition

from PART 1 - PAST

Published online by Cambridge University Press:  05 February 2016

James G. Greeno
Affiliation:
University of California at Berkeley
Timothy J. Nokes-Malach
Affiliation:
University of Pittsburgh
Michael A. Evans
Affiliation:
North Carolina State University
Martin J. Packer
Affiliation:
Universidad de los Andes, Colombia
R. Keith Sawyer
Affiliation:
University of North Carolina, Chapel Hill
Get access

Summary

This chapter reviews some contributions that we believe shed light on the emergence of a perspective – we refer to it as a situative perspective (Greeno & Engeström, 2014) – that has been a significant influence in the learning sciences during its first quarter of a century since it emerged as an interdisciplinary field in the late 1980s and early 1990s. The situative approach arose in response to perceived weaknesses in the cognitive theory of information processing, which had been the dominant theory of learning and cognition in the 1970s and 1980s. Today, the situative approach and the cognitivist approach both continue to remain influential in learning sciences research, although we believe the situative approach is more powerful and is now more widely used by learning scientists than a purely cognitivist approach.

The Cognitive Theory of Information Processing

In the mid-1980s, a cognitive theory of information processing was the leading perspective in the scientific study of cognition and learning. The scope of cognitive information processing theory is broad, as documented by Barsalou (1992), Lindsay and Norman (1977), Neisser (1967), and many others. The cognitive theory of information processing emerged in the 1960s and 1970s, and by the 1980s most scholars studying education and learning had acknowledged that it was a significant advance beyond the previously prevalent approaches of associationist theory and behaviorism. One significant advantage was the capability of modeling general patterns of information to represent schemata that students could acquire for solving problems and understanding concepts in much more detail than had been possible previously (cf. Greeno et al., 1978). For example, when Mayer and Greeno compared the learning outcomes of two kinds of instruction of the binomial formula – one more conceptual and the other more procedural – their explanation was limited to a hypothesis that an associative structure was less strongly connected internally in the procedural version and more strongly connected externally (that is, with other concepts) in the conceptual version. Hypotheses such as this one could be represented much more specifically with the theoretical methods and concepts of information-processing cognitive science (examples were reported and discussed in Greeno, 1983).

Two strands of the cognitivist research program are especially relevant to the development of a situative perspective: the theory of problem solving and the theory of language understanding.

Type
Chapter
Information
Publisher: Cambridge University Press
Print publication year: 2016

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

Ames, C. (1992). Classrooms: Goals, structures, and student motivations. Journal of Educational Psychology, 84, 261–271.CrossRefGoogle Scholar
Anderson, J. R. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.CrossRefGoogle Scholar
Anderson, J. R., Reder, L. M., & Simon, H. A. (1996, May). Situated learning and education. Educational Researcher, 25(4), 5–11.CrossRefGoogle Scholar
Barker, R. G. (1968). Ecological psychology: Concepts and methods for studying the environment of human behavior.Stanford, CA: Stanford University Press.Google Scholar
Barron, B. (2003). When smart groups fail. The Journal of Learning Sciences, 12, 307–359.CrossRefGoogle Scholar
Barsalou, L. W. (1992). Cognitive psychology: An overview for cognitive scientists.Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
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
Bronfenbrenner, U. (1979). The ecology of human development: Experiments by nature and design.Cambridge, MA: Harvard University Press.Google Scholar
Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Cambridge, MA: Harvard University Press.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Clancey, W. J., Sachs, P., Sierhuis, M., & van Hoof, R. (1998). BRAHMS: Simulating practice for work systems design.International Journal Human- Computer Studies, 49, 831–865.CrossRefGoogle Scholar
Clark, H. H. (1996). Using language.Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Clark, H. H., & Schaefer, E. F. (1989). Contributing to discourse. Cognitive Science, 13, 259–294.CrossRefGoogle Scholar
Cole, M., Griffen, P., & The Laboratory of Comparative Human Cognition (1987). Contextual factors in education.Madison, WI: Wisconsin Center for Education Research.Google Scholar
Dweck, C. S. (1986). Motivational processes affecting learning. American Psychologist, 41, 1040–1048.CrossRefGoogle Scholar
Elliot, A. J. (1999). Approach and avoidance motivation and achievement goals. Educational Psychologist, 34(3), 169–189.CrossRefGoogle Scholar
Engle, R. A. (2006). Framing interactions to foster generative transfer: A situative explanation of transfer in a community of learners classroom. Journal of the Learning Sciences, 15, 451–498.CrossRefGoogle Scholar
Engle, R. A. (2012). The productive disciplinary engagement framework. In Dai, D. Y. (Ed.), Design research on learning and thinking in educational settings (pp. 161–200). New York: Routledge.Google Scholar
Engle, R. A., Nguyen, P. D., & Mendelson, A. (2011). The influence of framing on transfer: initial evidence from a tutoring experiment. Instructional Science, 39, 603–628.CrossRefGoogle Scholar
Fraser, B. J. (1989). Twenty years of classroom climate work: Progress and prospect. Journal of Curriculum Studies, 21(4), 307–327.CrossRefGoogle Scholar
Gibson, J. J. (1966). The senses considered as perceptual systems.Boston: Houghton Mifflin.Google Scholar
Gibson, J. J. (1986). The ecological approach to visual perception.Hillsdale, NJ: Laawrence Erlbaum.Google Scholar
Goldman, S., Knudsen, J., & the Middle School Mathematics through Applications Project. (2002). Middle school mathematics: What every parent should know and can do.Stanford University.Google Scholar
Greeno, J. G. (1978). A study in problem solving. In Glaser, R. (Ed.), Advances in instructional psychology, Vol. 1.Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Greeno, J. G. (1983). Forms of understanding in mathematical problem solving. In Paris, S. G., Olson, G.M., & Stevenson, H. W. (Eds.), Learning and motivation in the classroom (pp. 83–111). Hillsdale NJ: Lawrence Erlbaum.Google Scholar
Greeno, J. G. (1989). A perspective on thinking. American Psychologist, 44(2), 134–141.Google Scholar
Greeno, J. G. (1992). Mathematical and scientific thinking in classrooms and other situations. In Halpern, D. (Ed.), Enhancement of higher–order thinking in science and mathematics education (pp. 39–62). Hillsdale, NJ: Lawrence Erlbaum.Google Scholar
Greeno, J. G. (1997, January/February). On claims that answer the wrong questions. Educational Researcher, 26(1), 5–17.Google Scholar
Greeno, J. G. (2006). Authoritative, accountable, positioning and connected general knowing: Progressive themes in understanding transfer. The Journal of the Learning Sciences, 15, 537–547.CrossRefGoogle Scholar
Greeno, J. G. (2011). A situative perspective on cognition and learning in interaction. In Koschmann, T. (Ed.), Theories of learning and studies of instructional practice (pp. 41–71). New York: Springer.Google Scholar
Greeno, J. G., & Engeström, Y. H. (2014). Learning in activity. In Sawyer, R. K. (Ed.), The Cambridge handbook of the learning sciences, 2nd ed. (pp. 128–150). Cambridge, UK: Cambridge University Press.Google Scholar
Greeno, J. G., James, C. T., DaPolito, F., & Polson, P. G. (1978). Organization and association. In J. G. Greeno (Ed.), Associative learning: A cognitive analysis (pp. 10–28). Englewood-Cliffs, NJ: Prentice-Hall.Google Scholar
Greeno, J. G., Magone, M. E. & Chaiklin, S. (1979). Theory of constructions and set in problem solving. Memory and Cognition, 7, 445–46l.CrossRefGoogle Scholar
Greeno, J. G., McDermott, R., Cole, K., Engle, R. A., Goldman, S., Knudsen, J., Lauman, B., & Linde, C. (1999). Research, reform, and aims in education: Modes of action in search of each other. In Lagemann, E. & Shulman, L. (Eds.), Issues in education research: Problems and possibilities (pp. 299–335). San Francisco: Jossey-Bass.Google Scholar
Greeno, J. G., & Moore, J. L. (1993). Situativity and symbols: A response to Vera and Simon. Cognitive Science, 17, 49–60.CrossRefGoogle Scholar
Greeno, J. G., & the Middle-School Mathematics through Applications Project (MMAP) Group (1998). The situativity of knowing, learning, and research. American Psychologist, 53, 5–26.CrossRefGoogle Scholar
Greeno, J. G., Smith, D. R., & Moore, J. L. (1993). Transfer of situated learning. In Detterman, D. K. & Sternberg, R. (Eds.), Transfer on trial: Intelligence, cognition, and instruction (pp. 99–167). Norwood, NJ: Ablex.Google Scholar
Gutierrez, K. D., & Rogoff, B. (2003). Cultural ways of learning: Individual traits or repertoires of practice. Educational Researcher, 32(5), 19–25.CrossRefGoogle Scholar
Hutchins, E. (1995a). Cognition in the wild. Cambridge, MA: MIT Press.Google Scholar
Hutchins, E. (1995b). How a cockpit remembers its speeds. Cognitive Science, 19, 265–288.CrossRefGoogle Scholar
Jordan, B. & Henderson, A. (1995). Interaction analysis: Foundations and practice. Journal of the Learning Sciences, 4, 39–103.CrossRefGoogle Scholar
Kintsch, W., & Greeno, J. G. (1985). Understanding and solving word arithmetic problems. Psychological Review, 92, 163–182.CrossRefGoogle ScholarPubMed
Kintsch, W., & van Dijk, T. A. (1978). Toward a model of text comprehension and production. Psychological Review, 85, 363–394.CrossRefGoogle Scholar
Klahr, D., Langley, P., & Neches, R. (Eds.). (1987). Production system models of learning and development. Cambridge, MA: MIT Press.Google Scholar
Lakatos, I. (1970). Falsification and the methodology of the scientific research programmes. In Lakatos, I. & Musgrave, A. (Eds.), Criticism and the growth of knowledge (pp. 91–195). Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Lave, J. (1988). Cognition in practice: Mind, mathematics, and culture in everyday life. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Lave, J., Murtaugh, M., & de la Rocha, O. (1984). The dialectic of arithmetic in grocery shopping. In Rogoff, B. & Lave, J. (Eds.), Everyday cognition: Its development in social context (pp. 67–94). Cambridge, MA: Harvard University Press.Google Scholar
Levinson, S. C. (1983). Pragmatics. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Lindsay, P. H., & Norman, D. A. (1977). Human information processing. New York: Academic.Google Scholar
Lobato, J. (2003). How design experiments can inform a rethinking of transfer and vice versa. Educational Researcher, 32(1), 17–20.CrossRefGoogle Scholar
Lobato, J. (2006). Alternative perspectives on the transfer of learning: History, issues, challenges for future research. Journal of the Learning Sciences, 15, 431–449.CrossRefGoogle Scholar
Meece, J. L., Blumenfeld, P. C., & Hoyle, R. H. (1988). Students’ goal orientations and cognitive engagement in classroom activities. Journal of Educational Psychology, 80, 514–523.CrossRefGoogle Scholar
Mitchell, S. D. (2003). Biological complexity and integrative pluralism. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Nasir, N. S., & Cooks, J. (2009). Becoming a hurdler: How learning settings afford identities. Anthropology & Education Quarterly, 40(1), 41–61.CrossRefGoogle Scholar
Nathan, M. J., & Sawyer, K. (2014). Foundations of the learning sciences. In Sawyer, K. (Ed.), The Cambridge handbook of the learning sciences, 2nd ed. (pp. 21–43). New York: Cambridge University Press.Google Scholar
Neisser, U. (1967). Cognitive psychology. Englewood Cliffs, NJ: Prentice-HallGoogle Scholar
Newell, A., & Simon, H. A. (1972). Human problem solving, Vol. 104, No. 9. Englewood Cliffs, NJ: Prentice-Hall.
Nilsson, N. J. (1971), Problem-solving methods in artificial intelligence. New York: McGraw-Hill.Google Scholar
Nokes, T. J., Schunn, C. D., & Chi, M. T. H. (2010). Problem solving and human expertise. In Peterson, P., Baker, E., & McGraw, B. (Eds.), International encyclopedia of education, Vol. 5 (pp. 265–272). Oxford: Elsevier.Google Scholar
Nokes-Malach, T. J., Meade, M. L., & Morrow, D. G. (2012). The effect of expertise on collaborative problem solving. Thinking & Reasoning, 18(1), 32–58.CrossRefGoogle Scholar
Nokes-Malach, T. J., & Mestre, J. (2013). Toward a model of transfer as sense-making. Educational Psychologist, 48(3), 184–207.CrossRefGoogle Scholar
Reeve, J., Jang, H., Carrell, D., Jeon, S., & Barch, J. (2004). Enhancing students’ engagement by increasing teachers’ autonomy support. Motivation and Emotion, 28, 147–169.CrossRefGoogle Scholar
Riley, M. S., & Greeno, J. G. (1988). Developmental analysis of understanding language about quantities and of solving problems. Cognition and Instruction, 5, 49–101.CrossRefGoogle Scholar
Rogoff, B. (1998). Cognition as a collaborative process. In Kuhn, D. & Siegler, R. S. (Eds.), Cognition, perception and language, Vol. 2. (pp. 679–744). Handbook of Child Psychology, 5th ed., Damon, W. (Ed.). New York: John Wiley & Sons.Google Scholar
Rommetveit, R. (1974). On message structure: A framework for the study of language and communication. New York: John Wiley & Sons.Google Scholar
Roschelle, J., & Teasley, S. (1995). The construction of shared knowledge in collaborative problem solving. In O'Malley, C. E. (Ed.), Computer supported collaborative learning (pp. 69–97). Heidelberg: Springer-Verlag.Google Scholar
Schank, R. C. (1973). Identification of conceptualizations underlying natual langage, In Schank, R. C. & Colby, K. M. (Eds.), Computer models of thought and language (pp. 187–247). San Francisco: W. H. Freeman.Google Scholar
Scribner, S. (1984). Studying working intelligence. In Rogoff, B. & Lave, J. (Eds.), Everyday cognition: Its development in social context (pp. 9–40). Cambridge, MA: Harvard University Press.Google Scholar
Seely Brown, J., & Duguid, P. (2002). The social life of information. Cambridge, MA: Harvard Business School Press.Google Scholar
Stahl, G. (2005). Group cognition in computer assisted collaborative learning. Journal of Computer Assisted Learning, 21(2), 71–90.CrossRefGoogle Scholar
Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. New York: Cambridge University Press.Google Scholar
VanLehn, K. (1989). Problem solving and cognitive skill acquisition. In Posner, M. I. (Ed.), Foundations of cognitive science (pp. 526–579). Cambridge, MA: M.I.T Press.Google Scholar
Wagner, J. F. (2006). Transfer in pieces. Cognition and Instruction, 24(1), 1–71.CrossRefGoogle Scholar
Wenger, E. (2000). Communities of practice: Learning, meaning, and identity. Cambridge, UK: Cambridge University Press.Google Scholar
Wertsch, J. V. (1985). Vygotsky and the social formation of mind. Cambridge, MA: Harvard University Press.Google Scholar
Winograd, T. (1973). A procedural model of language understanding. In Schank, R. C. & Colby, K. M. (Eds.), Computer models of thought and language (pp. 152–186). San Francisco: W. H. Freeman.Google Scholar

Save book to Kindle

To save this book to your Kindle, first ensure coreplatform@cambridge.org 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
×