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Guidon-manage revisited: A socio-technical systems approach

  • William J. Clancey
Invited Papers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)

Abstract

Until the late 1980s, ITS research proceeded in a harmonious way, with almost universal agreement within the community about the nature of human knowledge and learning. With the rise of situated cognition theories, considerable confusion has developed about theories of intelligence, when and how formal subject matter theories should be taught, and the relation of instructional technology to human interactions. Now, after several years of forming a new interdisciplinary community, methods for developing instructional programs can be articulated that emphasize developing programs that fit in the classroom and workplace. These methods place previous design processes into sharp relief and help us understand situated cognition claims about the relation of theory and practice.

Keywords

Participatory design computer-supported collaborative work socio-technical systems situated learning technical rationality glass box design 

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References

  1. Bamberger, J. and Schön, D.A. 1983. Learning as reflective conversation with materials: Notes from work in progress. Art Education, March.Google Scholar
  2. Bartlett, F.C. [1932] 1977. Remembering-A Study in Experimental and Social Psychology. Cambridge: Cambridge University Press. Reprint.Google Scholar
  3. Brown, J. S., Collins, A., and Duguid, P. 1988. Situated cognition and the culture of learning. IRL Report No. 88-0008. Shorter version appears in Educational Researcher, 18(1), February, 1989.Google Scholar
  4. Buchanan, B. G., and Shortliffe, E. H. 1984. Rule-Based Expert Systems: The MYCIN Experiments of the Heuristic Programming Project. Reading: Addison WesleyGoogle Scholar
  5. Clancey, W. J. 1988a. Acquiring, representing, and evaluating a competence model of diagnosis. In M. Chi, R. Glaser, & M. Farr (Ed.), The Nature of Expertise, pp. 343–418.Google Scholar
  6. Clancey, W. J. 1987. Knowledge-Based Tutoring: The GUIDON Program. Cambridge: MIT Press.Google Scholar
  7. Clancey, W. J. 1988b. The knowledge engineer as student Metacognitive bases for asking good questions. In H. Mandl, & A. Lesgold (Ed.), Learning Issues in Intelligent Tutoring Systems, Springer-Verlag.Google Scholar
  8. Clancey, W.J. 1991a. Why today's computers don't learn the way people do. In P. Flach and R. Meersman (editors), Future Directions in Artificial Intelligence. Amsterdam: Elsevier, pp. 53–62.Google Scholar
  9. Clancey, W.J. 1991b. Review of Rosenfield's “The Invention of Memory,” Artificial Intelligence, 50(2):241–284,1991.Google Scholar
  10. Clancey, W.J. 1991c. The frame of reference problem in the design of intelligent machines. In K. vanLehn (ed), Architectures for Intelligence: The TwentySecond Carnegie Symposium on Cognition, Hillsdale: Lawrence Erlbaum Associates, pp. 357–424.Google Scholar
  11. Clancey, W.J. 1991d. Invited talk. AI Communications—The European Journal on Artificial Intelligence 4(1):4–10.Google Scholar
  12. Clancey, W.J. 1991e. Situated Cognition: Stepping out of Representational Flatland. AI Communications—The European Journal on Artificial Intelligence 4(2/3):109–112.Google Scholar
  13. Clancey, W.J. 1992. Model construction operators. Artificial Intelligence, 53(1): 1–115.Google Scholar
  14. Clancey, W.J. in press. Representations of knowing—in defense of cognitive apprenticeship. To appear in the Journal of AI and Education.Google Scholar
  15. Clancey, W.J. (in preparation a). Interactive control structures: Evidence for a compositional neural architecture. Submitted for publication.Google Scholar
  16. Clancey, W.J. (in preparation b). A Boy Scout, Toto, and a bird: How situated cognition is different from situated robotics. A position paper prepared for the NATO Workshop on Emergence, Situatedness, Subsumption, and Symbol Grounding. To appear in a special issue of the AI Magazine, Brooks and Steels (eds).Google Scholar
  17. Clancey, W.J. (in preparation c). The knowledge level reconsidered: Modeling sociotechnical systems. To appear in The International Journal of Intelligent Systems, special issue on knowledge acquisition, edited by Ken Ford.Google Scholar
  18. Clancey, W.J. (in preparation d). Notes on “Epistemology of a rule-based expert system” and “Heuristic classification.” To appear in a special issue of most influential papers of Artificial Intelligence.Google Scholar
  19. Eckert, P. 1989. Jocks and Burnouts. New York: Teachers College Press.Google Scholar
  20. Edelman, G.M. 1987. Neural Darwinism: The Theory of Neuronal Group Selection. New York: Basic Books.Google Scholar
  21. Ehn, P. 1988. Work-Oriented Design of Computer Artifacts. Stockholm: Arbeslivscentrum.Google Scholar
  22. Floyd, C. 1987. Outline of a paradigm shift in software engineering. In Bjerknes, et al., (eds) Computers and Democracy—A Scandinavian Challenge, p. 197.Google Scholar
  23. Freeman, W.J. 1991. The Physiology of Perception. Scientific American, (February), 78–85.Google Scholar
  24. Goodman, P. 1971. Speaking and Language: Defence of Poetry. New York: Vintage Books.Google Scholar
  25. Greenbaum J. and Kyng, M. 1991. Design at Work: Cooperative design of computer systems. Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  26. Gregory, B. 1988. Inventing Reality: Physics as Language. New York: John Wiley & Sons, Inc.Google Scholar
  27. Hasling, D., Clancey, W. J., & Rennels, G. 1983. Strategic explanations in consultation. International Journal of Man-Machine Studies, 20(1):3–19.Google Scholar
  28. Hughes, J. Randall, D., and Shapiro, D. 1991. CSCW: Discipline or Paradigm? A sociological perspective. In L. Bannon, M. Robinson, and K. Schmidt (eds), Proceedings of the Second European Conference on Computer-Supported Cooperative Work. Amsterdam, pp. 309–323.Google Scholar
  29. Johnson, W.B. 1988. Developing expert system knowledge bases in technical training environments. In J. Psotka, D. Massey, & S. Mutter (eds), Intelligent Tutoring Systems: Lessons Learned, Hillsdale, NJ: Lawrence Erlbaum Publishers, 21–33.Google Scholar
  30. Jordan, B. 1990. Technology and the Social Distribution of Knowledge. In J. Coreil and D. Mull (eds), Anthropology and Primary Health Care. Westview Press, Boulder, pp. 98–120.Google Scholar
  31. Kling, R. 1991. Cooperation, coordination and control in computer-supported work. Communications of the ACM, 34(12)83–88.Google Scholar
  32. Lakoff, G. 1987. Women, Fire, and Dangerous Things: What Categories Reveal about the Mind. Chicago: University of Chicago Press.Google Scholar
  33. Lave, J. and Wenger, E. 1991. Situated Learning: Legitimate Peripheral Participation. Cambridge: Cambridge University Press.Google Scholar
  34. Linde, C. 1991. What's next? The social and technological management of meetings. Pragmatics, 1, 297–318.Google Scholar
  35. London, B., and Clancey, W.J. 1982. Plan recognition strategies in student modeling: Prediction and description. AAAI-82, pp. 335–338.Google Scholar
  36. Murray, T. and Woolf, B. (in preparation) Encoding domain and tutoring knowledge via a tutoring construction kit. Submitted to AAAI-92.Google Scholar
  37. Roschelle, J. and Clancey, W. J. (in preparation) Learning as Neural and Social. Presented at AERA91, Chicago. To appear in a special issue of the Educational Psychologist.Google Scholar
  38. Rodolitz, N. S., and Clancey, W. J. 1989. GUIDON-MANAGE: teaching the process of medical Diagnosis. In D. Evans, & V. Patel (eds), Medical Cognitive Science. Cambridge: Bradford Books, pp. 313–348.Google Scholar
  39. Richer, M., and Clancey, W. J. 1985. GUIDON-WATCH: A graphic interface for viewing a knowledge-based system. IEEE Computer Graphics and Applications, 5(11):51–64.Google Scholar
  40. Schön, D.A. 1987. Educating the Reflective Practitioner. San Francisco: Jossey-Bass Publishers.Google Scholar
  41. Stefik, M. and Conway, L. 1988. Towards the principled engineering of knowledge. In R. Engelmore (ed), Readings From the AI Magazine, Volumes 1–5, 1980–85. Menlo Park, CA: AAAI Press, pp. 135–147.Google Scholar
  42. Tyler, S. 1978. The Said and the Unsaid: Mind, Meaning, and Culture. New York: Academic Press.Google Scholar
  43. Wenger, E. 1990. Toward a theory of cultural transparency: Elements of a social discourse of the visible and the invisible. PhD. Dissertation in Information and Computer Science, University of California, Irvine.Google Scholar
  44. Wilkins, D. C., Clancey, W. J., & Buchanan, B. G. 1988. On using and evaluating differential modeling in intelligent tutoring and apprentice learning systems. In J. Psotka, D. Massey, & S. Mutter (eds), Intelligent Tutoring Systems: Lessons Learned, Hillsdale, NJ: Lawrence Erlbaum Publishers, pp. 257–284.Google Scholar
  45. Winograd, T. and Flores, F. 1986. Understanding Computers and Cognition: A New Foundation for Design. Norwood: Ablex.Google Scholar
  46. Wynn, E. 1991. Taking Practice Seriously. In J. Greenbaum and M. Kyng (eds), Design at Work: Cooperative design of computer systems. Hillsdale, NJ: Lawrence Erlbaum Associates, pp. 45–64.Google Scholar
  47. Zuboff, S. 1988. In the Age of the Smart Machine: The future of work and power. New York: Basic Books, Inc.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • William J. Clancey
    • 1
  1. 1.Institute for Research on LearningPalo Alto

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