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Toward a Theory of Curriculum for Use in Designing Intelligent Instructional Systems

  • Alan Lesgold
Part of the Cognitive Science book series (COGNITIVE SCIEN)

Abstract

Implicit in the approaches being taken by current efforts to create intelligent computer-based instruction is the notion that curriculum is almost an epiphenomenon of knowledge-driven instruction. Early computer-based instruction had little control structure other than an absolutely rigid curriculum and was insensitive to the subtleties of different students’ partial knowledge. As a result there was a reaction in the direction of representing the students’ knowledge as a subset of the target or goal knowledge to be taught and simply deciding de novo after each piece of instruction what piece of missing knowledge to teach the student. I am convinced that goal knowledge is as important to intelligent machine activity as it is to human activity and that it also must be well understood and explicitly represented in an instructional system if that system is to be successful in fostering learning.1 This chapter presents an architecture for representing curriculum or goal knowledge in intelligent tutors and is thus a first step toward a theory of curriculum that can inform the design of such systems. To illustrate one way in which such a theory can sharpen our ideas about learning and instruction, the later part of the chapter focuses on the concept of prerequisite that is the basis for existing computer-assisted instruction and shows how that concept has been inadequate in the past.

Keywords

Intelligent Tutor System Instructional System Resistor Network Goal Structure Internal Coherence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Anderson, J. R., Boyle, C. F., Farrell, R., & Reiser, B. J. (1984). Cognitive principles in the design of computer tutors (Report No. ONR-84–1). Pittsburgh, PA: Carnegie-Mellon University, Advanced Computer Tutoring Project.Google Scholar
  2. Bonar, J. (1985). Bite-sized intelligent tutoring. Technical Report. Pittsburgh, PA: University of Pittsburgh, Learning Research and Development Center, University of Pittsburgh.Google Scholar
  3. Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.CrossRefGoogle Scholar
  4. Brown, J. S., & VanLehn, K. (1980). Repair theory: A generative theory of bugs in procedural skills. Cognitive Science, 4, 379–426.CrossRefGoogle Scholar
  5. Burton, R. R., & Brown, J. S. (1982). An investigation of computer coaching for informal learning activities. In D. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems (pp. 79–98). Orlando, FL: Academic Press.Google Scholar
  6. Carey, S. (1985). Are children fundamentally different kinds of thinkers and learners than adults? In S. F. Chipman, J. W. Segal, & R. Glaser (Eds.), Thinking and learning skills: Vol. 2. Research and open questions. Hillsdale, NJ: Lawrence Erlbaum Assoc.Google Scholar
  7. Chase, W. G., & Simon, H. A. (1973). Perception in chess. Cognitive Psychology, 4, 55–81.CrossRefGoogle Scholar
  8. Chi, M. T. H., Feltovich, P., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.CrossRefGoogle Scholar
  9. Gagné, R. M. (1962). The acquisition of knowledge. Psychological Review, 69, 355–365.CrossRefGoogle Scholar
  10. Gagné, R. M. (1971). Conditions of learning. New York: Holt Rinehart & Winston.Google Scholar
  11. Glaser, R. (1984). Education and thinking: The role of knowledge. American Psychologist, 39, 93–104.CrossRefGoogle Scholar
  12. Glaser, R., Lesgold, A., & Lajoie, S. (in press). Toward a cognitive theory for the measurement of achievement. In R. R. Ronning, J. Glover, J. C. Conoley, & Witt, J. C. (Eds.), The influence of cognitive psychology on testing. Hillsdale, NJ: Lawrence Erlbaum Assoc.Google Scholar
  13. Goldberg, A., & Robson, D. (1983). Smalltalk-80: The language and its implementation. Reading, MA: Addison-Wesley.MATHGoogle Scholar
  14. Goldstein, L, & Carr, B. (1977, October). The computer as coach: An athletic paradigm for intellectual education. Proceedings of the 1977 Annual Conference (pp. 227–233). Seattle, WA: Association for Computing Machinery.Google Scholar
  15. de Groot, A. D. (1965). Thought and choice in chess. The Hague: Mouton.Google Scholar
  16. Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.CrossRefGoogle Scholar
  17. Lesgold, A. M., Lajoie, S., Eastman, R., Eggan, G., Gitomer, D., Glaser, R., Greenberg, L., Logan, D., Magone, M., Weiner, A., Wolf, R., & Yengo, L. (1986, April). Cognitive task analysis to enhance technical skills training and assessment. Technical Report. Pittsburgh, PA: University of Pittsburgh, Learning Research and Development Center.Google Scholar
  18. Shaughnessy, M. (1977). Errors and expectations. New York: Oxford.Google Scholar
  19. Stefik, M., & Bobrow, D. (1986). Object-oriented programming: Themes and variations. AI Magazine, 6, 40–62.Google Scholar
  20. VanLehn, K. (1983). On the representation of procedures in repair theory. In H. P. Ginsburg (Ed.), The development of mathematical thinking (pp. 197–252). Orlando, FL: Academic Press.Google Scholar
  21. Young, R. M. & O’Shea, T. (1981). Errors in children’s subtraction. Cognitive Science, 5 152–177.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Alan Lesgold

There are no affiliations available

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