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Instructional Science

, Volume 17, Issue 4, pp 281–307 | Cite as

Skill-oriented task sequencing in an intelligent tutor for basic algebra

  • David McArthur
  • Cathy Stasz
  • John Hotta
  • Orli Peter
  • Christopher Burdorf
Article

Abstract

As part of a project to develop an intelligent computer tutor for basic algebra, we have been investigating task sequencing. In this paper we present an approach to task sequencing that is based on a component-skills view of intelligence and learning. We postulate that tutors use inferences about past and present student performance to determine a current skill set that will be the new target for learning. The skill set is then used as a basis for generating tasks that should elicit those skills. Current skill sets are modified slowly over time so that lessons appear coherent and well-planned. We first describe the approach at a general level, where it can be viewed as a cognitive model of human task sequencing. Then we discuss the implementation of the model in our intelligent algebra tutoring system.

Keywords

General Level Student Performance Cognitive Model Task Sequencing Basic Algebra 
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. (1982). Acquisition of cognitive skill. Psychological Review, 89, 369–406.Google Scholar
  2. Anderson, J., Boyle, D. F. and Yost G. (1985). The geometry tutor. Proceedings of the Ninth International Joint Conference on Artificial Intelligence.Google Scholar
  3. Barr, A. M., Beard, M. and Atkinson, R. C. (1976). The computer as a tutorial laboratory: the Stanford BIP project. International Journal of Man-Machines Studies, 8, 567–596.Google Scholar
  4. Bell, A. W., Costello, J. and Kuchemann, D. E. (1983). A review of research in mathematical education (Part A). Berks., U.K.: NFER-Nelson.Google Scholar
  5. Block, J. and Burns, R. (1976). Mastery learning. Review of Research in Education, 4, 3–49.Google Scholar
  6. Brown, J. S. and Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science 2, 155–192.Google Scholar
  7. Brown, J. S., Collins, A. A. and Harris, G. (1978). Artificial intelligence and learning strategies. In H., O'Neil (Ed.), Learning strategies. New York: Academic Press.Google Scholar
  8. Burton, R. R. and Brown, J. S. (1982). An investigation of computer coaching. In D. H., Sleeman and J. S., Brown (Eds.), Intelligent tutoring systems (pp. 79–98). New York: Academic Press.Google Scholar
  9. Chambers, J. A. and Sprecher, J. W. (1980). Computer-assisted instruction: current trends and critical issues. Communications of the ACM, 23, 332–342.Google Scholar
  10. Clancey, W. J. (1979). Tutoring rules for guiding a case method dialogue. International Journal of Man-Machine Studies, 11, 25–49.Google Scholar
  11. Clancey, W. J. (1983). “GUIDON”. Journal of Computer-Based Instruction, 10, (1 and 2), 8–15.Google Scholar
  12. Collins, A. and Brown, J. S. (1986). The computer as a tool for learning through reflection. In H., MandI and A., Lesgold (Eds.), Learning issues for intelligent tutoring systems. New York: Springer-Verlag.Google Scholar
  13. Collins, A., Brown, J. S. and Newman, S. E. (1987). Cognitive apprenticeship: teaching the craft of reading, writing, and mathematics. In L. B., Resnick (Ed.), Cognition and instruction: issues and agendas. Hillsdale, N. J.: Lawrence Erlbaum Associates.Google Scholar
  14. Collins, A. and Stevens, A. (1981a). Goals and strategies of effective teachers. In R., Glaser (Ed.), Advances in instructional psychology (Vol 2). Hillsdale, N. J.: Lawrence Erlbaum Associates.Google Scholar
  15. Collins, A. and Stevens, A. (1981b). A cognitive theory of interactive teaching. In C. M., Reigeluth (Ed.), Instructional design theories and models: an overview. New York: Academic Press.Google Scholar
  16. Gagné, R. M. and Briggs, L. J. (1974). Principles of instructional design. New York: Holt, Rinehart and Winston.Google Scholar
  17. Goldstein, I. (1982). The genetic graph: a representation for the evolution of procedural knowledge. In D. H., Sleeman and J. S., Brown (Eds.), Intelligent tutoring systems. New York: Academic Press.Google Scholar
  18. Lawler, R. and Yazdani, M. (Eds.) (1987). Artificial intelligence and education: learning environments and intelligent tutoring systems. Norwood, N. J.: Ablex.Google Scholar
  19. Matz, M. (1982). Towards a process model for high school algebra errors. In D. H., Sleeman and J. S., Brown (Eds.), Intelligent tutoring systems. New York: Academic Press.Google Scholar
  20. McArthur, D., Stasz, C. and Hotta, J. (1987). Learning problem-solving skills in algebra. The Journal of Educational Technology Systems, 15(3), 303–324.Google Scholar
  21. McArthur, D. and Stasz, C. (1987). Tutoring techniques in algebra. Paper presented at the American Education Research Association national conference, Washington DC, April.Google Scholar
  22. Mitre Corporation (1976). An overview of the TICCIT program. Report M76–44, Washington: Mitre Corporation.Google Scholar
  23. Newell, A. and Simon, H. A. (1972). Human problem solving. Engelwood Cliffs, N. J.: Prentice-Hall.Google Scholar
  24. Ohlsson, S. (1986). Some principles of intelligent tutoring. Instructional Science, 14, 293–326.Google Scholar
  25. O'Shea, T. and Self, J. (1983). Learning and teaching with computers. New York: Prentice-Hall.Google Scholar
  26. Palmer, B. G. and Oldehoeft, A. E. (1975). The design of an instructional system based on problem-generators. International Journal of Man-Machine Studies, 7, 249–271.Google Scholar
  27. Peachy, D. and McCalla, G. (1986). Using planning techniques in intelligent tutoring systems. International Journal of Man-Machine Studies, 24, 77–98.Google Scholar
  28. Schoenfeld, A. H. (1983). Problem solving in the mathematics curriculum: a report, recommendations and an annotated bibliography. The Mathematical Association of America Notes, No. 1.Google Scholar
  29. Schank, R. and Abelson, R. (1977) Scripts, plans, goals, and understanding. Hillsdale, N. J.: Lawrence Erlbaum Associates.Google Scholar
  30. Schoenfeld, A. H. (1985). Mathematical problem solving. New York: Academic Press.Google Scholar
  31. Shute, V. and Glaser, R. (1986). An intelligent tutoring system for exploring principles of economics. Technical Report: Learning Research and Development Center, University of Pittsburgh, Pennsylvania.Google Scholar
  32. Sleeman, D. H. and Smith, M. J. (1981). Modeling student's problem solving. Artificial Intelligence, 16, 171–188.Google Scholar
  33. Smith, R. (1986). The altermate reality kit: an animated environment for creating simulations. Proceedings of the 1986 IEEE Computer Society Workshop on Visual Languages, 99–106.Google Scholar
  34. Stallings, J. A. and Stipek, D. (1986). Research on early childhood and elementary school teaching programs. In M., Wittrock (Ed.), Handbook of research on teaching (3rd edition). New York: MacMillan.Google Scholar
  35. Van Lehn, K. (1983). Felicity conditions for human skill acquisition: validating an AI-based theory. Doctoral dissertation, MIT, Cambridge, MA.Google Scholar
  36. VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence, 31, 1–40.Google Scholar
  37. Wenger, E. (1987). Artificial intelligence and tutoring systems. Los Altos, CA: Morgan and Kaufmann.Google Scholar
  38. Wescourt, K., Beard, M. and Gould, L. (1977). Knowledge-based adaptive curriculum sequencing for CAI: application of a network representation. Proceedings of ACM, 77, 234–240.Google Scholar
  39. Woolf, B. and McDonald, D. (1984). Building a computer-tutor: design issues. IEEE Computers, September, 61–73.Google Scholar

Copyright information

© Kluwer Academic Publishers 1988

Authors and Affiliations

  • David McArthur
    • 1
  • Cathy Stasz
    • 1
  • John Hotta
    • 1
  • Orli Peter
    • 1
  • Christopher Burdorf
    • 1
  1. 1.The RAND CorporationSanta MonicaU.S.A.

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