Advertisement

Problem Solving and Explanation in Intelligent Tutoring Systems: Issues for Future Research

  • Brian J. Reiser
Conference paper
Part of the NATO ASI Series book series (volume 96)

Abstract

The key to building effective intelligent tutoring systems is the representation of knowledge in the tutor’s problem solver. The problem solving knowledge determines the reasoning of students that the system can understand and the type of feedback the system can provide. We discuss the form of problem solving knowledge in intelligent tutors and the use of problem solving knowledge to provide guidance and feedback. We argue that model tracing tutors can be extended by building more underlying knowledge into their rule bases, and briefly describe GIL, a programming tutor built upon this elaborated model. We describe some of the current issues facing intelligent tutoring research, including the integration of rule-based and qualitative reasoning, timing and content of feedback, pedagogical strategies, and human- computer interface issues.

Keywords

ACT* addition analogical retrieval Artificial Intelligence diagnosing domain knowledge error feedback GIL Graphical Instruction in LISP immediate feedback intelligent tutoring systems interface knowledge representation learning complex skills mental models methodology model tracing multiple views reasoning rules rule-based reasoning simulation student model task analysis tutoring dialogues 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Anderson, J. R., Boyle, C. F., Farrell, R. G., & Reiser, B. J. Cognitive principles in the design of computer tutors. In P. Morris (Ed.), Modelling cognition, New York, NY: Wiley. 1987.Google Scholar
  2. 2.
    Anderson, J. R., Boyle, C. F., & Reiser, B. J. Intelligent tutoring systems. Science, 228, 456–462. 1985.CrossRefGoogle Scholar
  3. 3.
    Anderson, J. R., Boyle, C. F., & Yost, G. The geometry tutor. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA. 1985.Google Scholar
  4. 4.
    Anderson, J. R., Farrell, R., & Sauers, R. (1985). Learning to program in LISP. Cognitive Science, 8, 87–129.CrossRefGoogle Scholar
  5. 5.
    Anderson, J. R., & Reiser, B. J. The LISP tutor. Byte, 10, 159–175. 1985.Google Scholar
  6. 6.
    Bloom, B. S. The 2 sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13, 4–16. 1984.Google Scholar
  7. 7.
    Bonar, J. G., & Cunningham, R. Bridge: Tutoring the programming process. In J. Psotka, L. D. Massey, & S. A. Mutter, Eds. Intelligent tutoring systems: Lessons learned. Erlbaum. 1988.Google Scholar
  8. 8.
    Bransford, J. D., Stein, B. S., Arbitman-Smith, R., & Vye, N. J. Improving thinking and learning skills: An analysis of three approaches. In Segal, J. W., Chipman, S. F., & Glaser R., Eds. Thinking and learning skills, Volume 1: Relating instruction to research. Erlbaum. 1985.Google Scholar
  9. 9.
    Brown, J. S. Learning by doing revisited for electronic learning environments. In M. A. White (Ed.), The future of electronic learning, Hillsdale, NJ: Erlbaum. 1983.Google Scholar
  10. 10.
    Bruner, J. S. The act of discovery. Harvard Educational Review, 31, 21–32. 1961.Google Scholar
  11. 11.
    Burton R.B. Diagnosing bugs in a simple procedural skill. In D.H. Sleeman & J.S. Brown (eds) Intelligent Tutoring Systems New York Academic, 157–183. 1982.Google Scholar
  12. 12.
    Carroll, J. M., & Carrithers, C. Blocking learner error states in a training wheels system. Human Factors, 26, 377–389. 1984.Google Scholar
  13. 13.
    Catrambone, R., & Carroll, J. M. Learning a word processing system with training wheels and guided exploration. Proceedings ofCHI+GI 87 Human Factors in Computing Systems. New York: ACM, 169–174. 1987.Google Scholar
  14. 14.
    Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. Self explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13,145–182.. 1991.CrossRefGoogle Scholar
  15. 15.
    Clancey, W. J. Tutoring rules for guiding a case method dialogue. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic Press. 1982.Google Scholar
  16. 16.
    Clancey, W. J. The epistemology of a rule-based expert system - A framework for explanation. Artificial Intelligence, 20, 215–251. 1983.CrossRefGoogle Scholar
  17. 17.
    Clancey, W. J. Knowledge-based tutoring: The Guidon program. Cambridge, MA: MIT Press. 1987.Google Scholar
  18. 18.
    Cohen, P. A., Kulik, J. A., & Kulik, C.-L. C. Educational outcomes of tutoring: A meta-analysis of findings. American Educational Research Journal, 19, 237–248. 1982.Google Scholar
  19. 19.
    Collins, A., & Brown, J. S. The computer as a tool for learning through reflection. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems. New York: Springer-Verlag. 1988.Google Scholar
  20. 20.
    Collins, A., Brown, J. S., & Newman, S. E. Cognitive apprenticeship: Teaching the craft of reading, writing, and mathematics. To appear in L. B. Resnick (Ed.), Cognition and instruction: Issues and agendas. Hillsdale, NJ: Erlbaum. (in press).Google Scholar
  21. 21.
    Collins, A., & Stevens, A. L. Goals and strategies of inquiry teachers. In R. Glaser (Ed.), Advances in Instructional Psychology, Volume 2. Hillsdale, NJ: Erlbaum. 1982.Google Scholar
  22. 22.
    Corbett, A. T., Anderson, J. R., & Patterson, E. J. Problem compilation and tutoring flexibility in the LISP Tutor. Proceedings of ITS-88: The International Conference on Intelligent Tutoring Systems, Montreal, pp. 423–429. 1988.Google Scholar
  23. 23.
    Escott, J. A., & McCalla, G. I. Problem solving by analogy: A source of errors in novice LISP programming. Proceedings of ITS-88: The International Conference on Intelligent Tutoring Systems, Montreal, pp. 312–319. 1988.Google Scholar
  24. 24.
    Faries, J. M., & Reiser, B. J. Access and use of previous solutions in a problem solving situation. Proceedings of the Tenth Annual Conference of the Cognitive Science Society, Montreal. 1988.Google Scholar
  25. 25.
    Fox, B. A. Cognitive and interactional aspects of correction in tutoring. Technical Report #88-2. Institute of Cognitive Science, University of Colorado, Boulder, Colorado. 1988.Google Scholar
  26. 26.
    Gentner, D. Analogical inference and analogical access. In A. Prieditis (Ed.), Analogica, Los Altos, CA: Morgan Kaufman. 1988.Google Scholar
  27. 27.
    Glaser, R., Raghavan, K., & Schauble, L. Voltaville, a discovery environment to explore the laws of DC circuits. Proceedings of ITS-88: The International Conference on Intelligent Tutoring Systems, Montreal, pp. 61–66. 1988.Google Scholar
  28. 28.
    Goldstein, I. P. The genetic graph: A representation for the evolution of procedural knowledge. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic Press. 1982.Google Scholar
  29. 29.
    Hammond, K. The use of remindings in planning. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Amherst, MA. 1986.Google Scholar
  30. 30.
    Hollan, J. D., Hutchins, E. L., & Weitzman, L. Steamer: An interactive inspectable simulation-based training system. Al Magazine, 5, 75–28. 1984.Google Scholar
  31. 31.
    Johnson, W. L. Intention-based diagnosis of errors in novice programs. Palo Alto, CA: Morgan Kaufman.. 1986.Google Scholar
  32. 32.
    Johnson, W. L., & Soloway, E. PROUST: An automatic debugger for Pascal programs. In G. Kearsley (Ed.), Artificial intelligence and instruction: Applications and methods. Reading, MA: Addison-Wesley. 1987.Google Scholar
  33. 33.
    Kolodner, J. L. Extending problem solver capabilities through case-based inference. Proceedings of the Fourth International Conference on Machine Learning, Irvine, CA, Morgan Kaufman, 167–178. 1987.Google Scholar
  34. 34.
    Lepper, M. R., & Chabay, R. W. Socializing the intelligent tutor: Bringing empathy to computer tutors. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems. New York: Springer-Verlag. 1988.Google Scholar
  35. 35.
    LeFevre, J. & Dixon, P. Do written instructions need examples? Cognition and Instruction, 3, 1–30. 1986.CrossRefGoogle Scholar
  36. 36.
    Miller, J. R. The role of human-computer interaction in intelligent tutoring systems. In M. C. Poison, & J. J. Richardson (Eds.), Foundations of intelligent tutoring systems. Hillsdale, NJ: Erlbaum. 1988.Google Scholar
  37. 37.
    Palincsar, A. S., & Brown, A. L. Reciprocal teaching of comprehension-fostering and comprehension-monitoring activities. Cognition and Instruction, 1, 117–175. 1984.CrossRefGoogle Scholar
  38. 38.
    Pirolli, P. L., & Anderson, J. R. The role of learning from examples in the acquisition of recursive programming skills. Canadian Journal of Psychology, 39, 240–272. 1985.CrossRefGoogle Scholar
  39. 39.
    Putnam, R. T. Structuring and adjusting content for students: A study of live and simulated tutoring of addition. American Educational Research Journal, 24, 13–48. 1987.Google Scholar
  40. 40.
    Reiser, B. J., Anderson, J. R., & Farrell, R. G. Dynamic student modelling in an intelligent tutor for LISP programming. Proceedings of the Ninth International Joint Conference on Artificial Intelligence, Los Angeles, CA. 1985.Google Scholar
  41. 41.
    Reiser, B. J., Friedmann, P., Gevins, J., Kimberg, D., Ranney, M., & Romero, A. A graphical programming language interface for an intelligent LISP tutor. Proceedings of CHI’88, Conference on Human Factors in Computing Systems, ACM, New York, pp. 3944. 1988.Google Scholar
  42. 42.
    Reiser, B. J., Friedmann, P., Kimberg, D., & Ranney, M. Constructing explanations from problem solving rules to guide the planning of programs. Proceedings of ITS-88: The International Conference on Intelligent Tutoring Systems, Montreal, pp. 222–229.Google Scholar
  43. 43.
    Reiser, B. J., Kimberg, D. Y., Lovett, M. C., & Ranney, M. Knowledge representation and explanation in GIL, an intelligent tutor for programming. To appear in J. Larkin, R. Chabay, & C. Scheftic (Eds.), Computer-assisted instruction and intelligent tutoring systems: Establishing communication and collaboration, Erlbaum. (in press).Google Scholar
  44. 44.
    Rissland, E. L., & Ashley, K. D. Hypotheticals as heuristic device. Proceedings of AAA1-86, Fifth National Conference on Artificial Intelligence, Phila, PA, pp. 289–297. 1986.Google Scholar
  45. 45.
    Roschelle, J. The Envisioning Machine: Facilitating students’ reconceptualization of motion. Paper presented at the Third International Conference on Artificial Intelligence and Education, Pittsburgh, PA. 1987.Google Scholar
  46. 46.
    Roschelle, J., & Greeno, J. G. Mental models in expert physics reasoning. Technical report #GK-2, School of Education, University of California, Berkeley. 1987.Google Scholar
  47. 47.
    Ross, B. H. Remindings and their effect in learning a cognitive skill. Cognitive Psychology, 16, 371–416. 1984.CrossRefGoogle Scholar
  48. 48.
    Ross, B. H. This is like that: The use of earlier problems and the separation of similarity effects. Journal of Experimental Psychology: Learning, Memory, and Cognition, 13, 629639. 1987.Google Scholar
  49. 49.
    Schank, R. C., & Farrell, R. Creativity in education: A standard for computer-based teaching. Machine-Mediated Learning. 1987.Google Scholar
  50. 50.
    Segal, J. W., Chipman, S. F., & Glaser R., Eds. Thinking and learning skills, Volume 1: Relating instruction to research. Erlbaum. 1985.Google Scholar
  51. 51.
    Shneiderman, B. Direct manipulation: A step beyond programming languages. IEEE Computer, 16, 57–69. 1983.CrossRefGoogle Scholar
  52. 52.
    Shute, V., Glaser, R., & Raghavan, K. Inference and discovery in an exploratory laboratory. To appear in P. L. Ackerman, R. J. Sternberg, & R. Glaser (Eds.), Learning and individual differences. San Francisco, CA: Freeman, (in press).Google Scholar
  53. 53.
    Sleeman, D. Assessing aspects of competence in basic algebra. In D. H. Sleeman & J. S. Brown (Eds.), Intelligent tutoring systems. London: Academic Press. 1982.Google Scholar
  54. 54.
    White, B. Y. ThinkerTools: Causal models, conceptual change, and science education. To appear in Cognition and Instruction, (in press).Google Scholar
  55. 55.
    White, B. Y., & Frederiksen, J. R. Qualitative models and intelligent learning environments. In R. Lawler & M. Yazdani (Eds.), Artificial intelligence and education, Volume 1. Norwood, NJ: Ablex. 1987.Google Scholar
  56. 56.
    White, B. Y., & Frederiksen, J. R. Causal model progressions as a foundation for intelligent learning environments. To appear in Artificial Intelligence, (in press).Google Scholar
  57. 57.
    Williams, M. D., Hollan, J. D., & Stevens, A. L. Human reasoning about a simple physical system. In D. Gentner & A. L. Stevens (Eds.), Mental models, Hillsdale, NJ: Erlbaum. 1983.Google Scholar
  58. 58.
    Zhu, X., & Simon, H. A. Learning mathematics from examples and by doing. Cognition and Instruction, 4, 137–166. 1987.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Brian J. Reiser
    • 1
  1. 1.Cognitive Science LaboratoryPrinceton UniversityPrincetonUSA

Personalised recommendations