Skip to main content

Towards Intelligent Tutoring Systems that Teach Knowledge Rather than Skills: Five Research Questions

  • Conference paper
Book cover New Directions in Educational Technology

Part of the book series: NATO ASI Series ((NATO ASI F,volume 96))

Abstract

This paper argues that five types of research are needed in order to develop a cognitive theory that can support the development of intelligent tutoring systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abelson, R. P. Whatever became of consistency theory? Personality and Social Psychology Bulletin, 9, 37–54. 1983.

    Article  Google Scholar 

  2. Abelson, R. P., Aronson, E., McGuire, W. J., Newcomb, T. M., Rosenberg, M. J., Tannenbaum, P. H. (Eds.), Theories of cognitive consistency: A sourcebook Chicago, I11.: Rand McNally. 1968.

    Google Scholar 

  3. Anderson, J. R. Acquisition of cognitive skill. Psychological Review, 89, 369–406. 1982.

    Article  Google Scholar 

  4. Anderson, J. R. Acquisition of proof skills in geometry. In R. S. Michalski, J. G. Carbonell, T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. Palo Alto, CA: Tioga Publishing Co. 1983a.

    Google Scholar 

  5. Anderson, J. R. The architecture of cognition. Cambridge, MA: Harvard University Press. 1983b.

    Google Scholar 

  6. Anderson, J. R. Knowledge compilation: The general learning mechanism. In R. S. Michalski, J. G. Carbonell, T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach (Vol. 11, pp. 289–310 ). Los Altos, CA: Morgan Kaufmann Pub., Inc. 1986.

    Google Scholar 

  7. Anderson, J. R., Boyle, C. F., Yost, G. The geometry tutor. Proceedings of the International Joint Conference on Artificial Intelligence, Los Angeles, 1–7. 1985.

    Google Scholar 

  8. Baroody, A. J. Gannon, K. E. The development of the commutativity principle and economical addition strategies. Cognition and Instruction, 1, 245–296. 1984.

    Article  Google Scholar 

  9. Brooks, L. W., Dansereau, D. F. Transfer of information: An instructional perspective. In S. M. Cormier, J. D. Hagman (Eds.), Transfer of learning: Contemporary research and applications. New York: Academic Press. 1987.

    Google Scholar 

  10. Burton, R.B. Diagnosing bugs in a simple procedural skill In D.H. Sleeman and J.S. Brown (eds) Intelligent Tutoring Systems. New York; Academic, 157–183. 1982.

    Google Scholar 

  11. Clancey, W. J. Knowledge-based tutoring. The GUIDON program. Cambridge, Mass.: MIT Press. 1987.

    Google Scholar 

  12. DeJong, G., & Mooney, R. Explanation-based learning: An alternative view. Machine Learning, 1, 145–176. 1986.

    Google Scholar 

  13. Egan, D. E., & Greeno, J. G. Acquiring cognitive structure by discovery and rule learning. Journal of Educational Psychology, 64, 85–97. 1973.

    Article  Google Scholar 

  14. Gardenfors, P. Knowledge in flux. Modelling the dynamics of epistemic states. Cambridge, MA.: MIT Press. 1988.

    Google Scholar 

  15. Gelman, R., & Gallistel, C. R. The child’s understanding of number. Cambridge, MA: Harvard University Press. 1978.

    Google Scholar 

  16. Gelman, R., & Meek, E. Preschoolers’ counting: Principle before skill. Cognition, 13, 343–359. 1983.

    Google Scholar 

  17. Gelman, R., & Meek, E. The notion of principle: The case of counting. In J. H. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 29–57 ). Hillsdale, NJ: Erlbaum. 1986.

    Google Scholar 

  18. Greeno, J. G., Riley, M. S., & Gelman, R. Conceptual competence and children’s counting. Cognitive Psychology, 16, 94–143. 1984.

    Article  Google Scholar 

  19. Haertel, H. A qualitative approach to electricity (Technical Report, August 1987 ). Palo Alto, Calif.: Institute for Research on Learning. 1987.

    Google Scholar 

  20. Harman, G. Change in view. Principles of reasoning. Cambridge, Mass.: MIT Press. 1986.

    Google Scholar 

  21. Hayes, P. J. The logic of frames. In R. J. Brachman & H. J. Levesque, (Eds.), Readings in knowledge representation. Los Altos, Calif.: Kaufmann. 1985/1979.

    Google Scholar 

  22. Hiebert, J. (Ed.). Conceptual and procedural knowledge: The case of mathematics. Hillsdale, NJ: Erlbaum. 1986.

    Google Scholar 

  23. Johnson-Laird, P. N. Mental models. Towards a cognitive science of language, inference, and consciousness. Cambridge, Mass.: Harvard University Press. 1983.

    Google Scholar 

  24. Katona, G. Organizing and memorizing: Studies in the psychology of learning and teaching. New York: Hafner Pub. Co. 1967.

    Google Scholar 

  25. Kieren, T. E. Personal knowledge of rational numbers: Its intuitive and formal development. In J. Hiebert & M. Behr, (Eds.), Number concepts and operations in the middle grades. Hillsdale, NJ: Erlbaum. 1988.

    Google Scholar 

  26. Klahr, D., Langley, P., & Neches, R. (Eds.). Production system models of learning and development Cambridge, MA: The MIT Press. 1987.

    Google Scholar 

  27. Kuhn, T. The structure of scientific revolutions, second ed. Chicago, Ill.: University of Chicago Press. 1970.

    Google Scholar 

  28. Laird, J. E., Rosenbloom, P. S., & Newell, A. Universal subgoaling and chunking: The automatic generation and learning of goal hierarchies. Boston, MA: Kluwer. 1986.

    Google Scholar 

  29. Langley, P. A general theory of discrimination learning. In D. Klahr, P. Langley, & R. Neches (Eds.), Production system models of learning and development (pp. 99 - 161 ). Cambridge, MA: The MIT Press. 1987.

    Google Scholar 

  30. Langley, P. & Jones, R. A computational model of scientific insight. In R. J. Sternberg, (Ed.), The nature of creativity. Contemporary psychological perspectives. Cambridge: Cambridge University Press. 1988.

    Google Scholar 

  31. Langley, P., Wogulis, J., & Ohlsson, S. Rules and principles in cognitive diagnosis. In N. Frederiksen (Ed.), Diagnostic monitoring of skill and knowledge acquisition. Hillsdale, NJ: Erlbaum. (in press).

    Google Scholar 

  32. Lenat, D. B. Toward a theory of heuristics. In R. Groner, M. Groner, & W. F. Bishop, (Eds.), Methods of heuristics. Hillsdale, NJ: Erlbaum. 1983.

    Google Scholar 

  33. Mayer, R. E., Stiehl, C. C., & Greeno, J. G. Acquisition of understanding and skill in relation to subjects’ preparation and meaningfiilness of instruction. Journal of Educational Psychology, 67, 331–350. 1975.

    Article  Google Scholar 

  34. McDermott, D. & Doyle, J. Non-monotonic logic 1. Artificial Intelligence, 13, 41–72. 1980.

    Article  MATH  MathSciNet  Google Scholar 

  35. Michener, E. R. Understanding understanding mathematics. Cognitive Science, 2, 361–383. 1978.

    Article  Google Scholar 

  36. Mitchell, T. M., Keller, R. M., & Kedar-Cabelli, S. T. Explanation-based generalization: A unifying view. Machine Learning, 1, 47–80. 1986.

    Google Scholar 

  37. Nesher, P. Are mathematical understanding and algorithmic performance related? For the Learning of Mathematics, 6, 3–9. 1986.

    Google Scholar 

  38. Newell, A., & Simon, H. A. Human problem solving. Englewood Cliffs, NJ: Prentice- Hall, Inc. 1972.

    Google Scholar 

  39. Nilsson, N. J. Problem-solving methods in artificial intelligence. New York: McGraw- Hill. 1971.

    Google Scholar 

  40. Novak, J. D. (Ed.), Proceedings of the Second International Seminar on Misconceptions and Educational Strategies in Science and Education, Vols. 1–111, July 26–29. Ithaca, NY: Cornell University. 1987.

    Google Scholar 

  41. Ohlsson, S. Competence and strategy in reasoning with common spatial concepts (Technical Report No. 4 ). Stockholm, Sweden: The Cognitive Seminar, Department of Psychology, University of Stockholm. 1980.

    Google Scholar 

  42. Ohlsson, S. Restructuring revisited. 1. Summary and critique of the Gestalt theory of problem solving. Scandinavian Journal of Psychology, 25, 65–78. 1984a.

    Article  Google Scholar 

  43. Ohlsson, S. Restructuring revisited. 11. An information processing theory of restructuring and insight. Scandinavian. Journal of Psychology, 25, 117–129. 1984b.

    Article  Google Scholar 

  44. Ohlsson, S. Transfer of training in procedural learning: A matter of conjectures and refutations? In L. Bole (Ed.), Computational models of learning (pp. 55–88 ). Berlin, Federal Republic of Germany: Springer-Verlag. 1987a.

    Google Scholar 

  45. Ohlsson, S. Truth versus appropriateness: Relating declarative to procedural knowledge. In D. Klahr, P. Langley, & R. Neches (Eds.), Production system models of learning and development (pp. 287–327 ). Cambridge, MA: The MIT Press. 1987b.

    Google Scholar 

  46. Ohlsson, S. Computer simulation and its impact on educational research and practice. International Journal of Educational Research, 72, 5–72. Oxford: Pergamon Press. 1988a.

    Google Scholar 

  47. Ohlsson, S. Mathematical meaning and applicational meaning in the semantics of fractions and related concepts. In J. Hiebeit & M. Behr, (Eds.), Number concepts and operations in the middle grades. Hillsdale, NJ: Erlbaum. 1988b.

    Google Scholar 

  48. Ohlsson, S. Trace analysis and spatial reasoning: An example of intensive cognitive diagnosis and its implications for testing. In N. Frederiksen, R. Glaser, A. M. Lesgold, & M. Shafto, (Eds.), Diagnostic monitoring of skill and knowledge acquisition. Hillsdale, NJ: Erlbaum. (in press).

    Google Scholar 

  49. Ohlsson, S., & Langley, P. Psychological evaluation of path hypotheses in cognitive diagnosis. In H. Mandl, & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems. New York: Springer-Verlag. 1988.

    Google Scholar 

  50. Ohlsson, S., & Rees, E. An information processing analysis of the function of conceptual understanding in the learning of arithmetic procedures (Tech. Report No. KUL-88-03). Pittsburgh, PA: University of Pittsburgh, Learning Research and Development Center. 1988.

    Google Scholar 

  51. Piaget, J. In P. H. Müssen, (Ed.), Handbook of child psychology. Vol 1: History, theory, and methods. New York, NY: Wiley. 1983.

    Google Scholar 

  52. Piaget, J. Experiments in contradiction. Chicago, Ill.: Chicago University Press. 1986.

    Google Scholar 

  53. 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, 8–14. 1985.

    Google Scholar 

  54. Resnick, L. B., & Omanson, S. F. Learning to understand arithmetic. In R. Glaser (Ed.), Advances in instructional psychology (Vol. 3, pp. 41 - 95 ). Hillsdale, NJ: Erlbaum. 1987.

    Google Scholar 

  55. Rose, D. & Langley, P. Chemical discovery as belief revision. Machine Learning, 7, 423–452. 1986.

    Google Scholar 

  56. Simon, H. A. The sciences of the artificial. Cambridge, Mass.: MIT Press. 1969.

    Google Scholar 

  57. Smith, D. A., Greeno, J. G., & Vitolo, T. M., A model of competence for counting. Cognitive Science. (in press).

    Google Scholar 

  58. VanLehn, K. Learning one subprocedure per lesson (Tech. Report No. ISL-10). Palo Alto, CA: Xerox PARC. 1985.

    Google Scholar 

  59. VanLehn, K. Arithmetic procedures are induced from examples. In J. H. Hiebert (Ed.), Conceptual and procedural knowledge: The case of mathematics (pp. 133–179 ). Hillsdale, NJ: Erlbaum. 1986.

    Google Scholar 

  60. VanLehn, K. Student modelling. In M. C. Poison & J. J. Richardson, (Eds.), Foundations of intelligent tutoring systems. Hillsdale, NJ: Erlbaum. 1988.

    Google Scholar 

  61. Vergnaud, G. Multiplicative structures. In J. Hiebert & M. Behr, (Eds.), Number concepts and operations in the middle grades. Hillsdale, NJ: Erlbaum. 1988.

    Google Scholar 

  62. Wenger, E. Artificial intelligence and tutoring systems. Los Altos, Calif.: Morgan Kaufmann, Inc. 1987.

    Google Scholar 

  63. White, B. Designing computer games to help physics students understand Newton’s laws of motion. Cognition and Instruction, 1, 1–4. 1984.

    Article  Google Scholar 

  64. Winograd, T. Frame representations and the declarative/procedural controversy. In D. G. Bobrow & A. Collins (Eds.), Representation and understanding. Studies in cognitive science (pp. 185–210 ). New York: Academic Press. 1975.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ohlsson, S. (1992). Towards Intelligent Tutoring Systems that Teach Knowledge Rather than Skills: Five Research Questions. In: Scanlon, E., O’Shea, T. (eds) New Directions in Educational Technology. NATO ASI Series, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77750-9_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-77750-9_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-77752-3

  • Online ISBN: 978-3-642-77750-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics