Skip to main content
SpringerLink
Log in

Towards a Computational Model of Tutoring

  • Conference paper
Adaptive Learning Environments

Part of the book series: NATO ASI Series ((NATO ASI F,volume 85))

Abstract

This chapter addresses several issues around successful integration of instructional science and computer science. It addresses issues of building computational models of tutoring and incorporating instructional principles. The first barrier to overcome towards this integration is development of principled programs in which cognitive principles about learning and teaching are realized at a level of granularity consistent with building computational models. Such cognitive studies would facilitate fine-grained modelling of learning and teaching. The second barrier to overcome is the gap between the two disciplines, in terms of goals, motivations, literature, and even defining concepts. This situation suggests that a large effort should go into two areas: research on understanding basic principles behind learning and teaching, and the establishment of clearer lines of communication between instructional and computer scientists. This chapter addresses both these issues.

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

Access this chapter

Log in via an institution
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. Anderson, J.R.: Analysis of student performance with the Lisp tutor. In: Diagnostic monitoring of skill and knowledge acquisition (N. Frederickson et al., eds.). Hillsdale, NJ: Lawrence Erlbaum Associates 1990

    Google Scholar 

  2. Anderson, J., & Reiser, B.: The Lisp tutor. Byte, 10, pp. 159–175 (1986)

    Google Scholar 

  3. Anderson, J.R., Boyle, C.F., Corbett, A.T., & Lewis, M.W.: Cognitive modeling and knowledge-based tutoring. Artificial Intelligence, 42, pp. 7–50 (1990)

    Article  Google Scholar 

  4. Bloom, B.S.: The 2-sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13., 6, pp. 4–16 (1984)

    Google Scholar 

  5. . Bowscher, J.E.: Educating America: Lessons learned in the nation’s corporations. New York: Wiley & Sons 1989

    Google Scholar 

  6. Clancey, W.J.: A practical authority shell for apprenticeship learning. Proceedings of the International Conference on Intelligent Tutoring Systems (ITS-88). University of Montreal, Canada, June 1988

    Google Scholar 

  7. Corbett, A., & Anderson, J.: The effect of feedback control on learning to program with the Lisp tutor. Proceedings of the 12th Annual Meeting of the Cognitive Science Society, pp. 702–708. Cambridge, MA 1990.

    Google Scholar 

  8. Dillenbourg, P.: The language shift: A mechanism for triggering metacognitive activities. In: Foundations and frontiers in instructional computing systems (P. Winne, & M. Jones, eds.). New York: Springer-Verlag 1991

    Google Scholar 

  9. Duckworth, E., Kelley, J., & Wilson, S.: AI goes to school. Academic Computing,pp. 6–10, 38–43, 6263, November 1987

    Google Scholar 

  10. Goldman, H.D.: Instructional systems development in the United States Air Force. In: Instructional development: The state of the art II (R. Bass, & C. Dills, eds.). Dubuque: Kendall/Hunt 1984

    Google Scholar 

  11. Johnson, L., & Soloway, E.M.: Intention-based diagnosis of programming errors. Proceedings of the National Conference of Artificial Intelligence, pp. 369–380, Austin, Texas 1984

    Google Scholar 

  12. Lesgold, A., Laijoie, S.P., Bunzo, M., & Eggan, G.: A coached practice environment for an electronics troubleshooting job. In: Computer assisted instruction and intelligent tutoring systems (J. Larkin, R. Chabay, & C. Shefic, eds.). Hillsdale, NJ: Lawrence Erlbaum Associates 1990

    Google Scholar 

  13. Lewis, M., McArthur, D., Stasz, C., & Zmuidzinas, M.: Discovery-based tutoring in mathematics. In: Working notes: Artificial Intelligence Spring Symposium, AAAI, Palo Alto, CA 1990

    Google Scholar 

  14. Murray, T., & Woolf, B.: A knowledge acquisition framework facilitating multiple tutoring strategies. Working notes: Symposium on Knowledge-based Environments for Learning and Teaching, pp. 120–127, AAAI: Menlo Park, CA 1990

    Google Scholar 

  15. Schooler, L. & Anderson, J.: The disruptive potential of immediate feedback. Proceedings of the 12th Annual Meeting of the Cognitive Science Society, pp. 796–803, Cambridge, MA 1990

    Google Scholar 

  16. Servan-Schreiber, D.: From intelligent tutoring to computerized psychotherapy. Proceedings of the Sixth National Conference on Artificial Intelligence (AAAI-87), pp. 66–71, Los Altos, CA: Morgan Kaufmann 1987

    Google Scholar 

  17. Shute, V.: Rose garden promises of intelligent tutoring systems: Blossom or thorn? Paper presented at the Space Operations, Applications and Research (SOAR) Symposium (Contact V. Shute, AFHRL, Brooks Air Force Base, TX 78235–5601) 1990

    Google Scholar 

  18. Shute, V.J., Glaser, R., & Raghavan, K.: Inference and discovery in an exploratory laboratory. In: Learning and individual differences (P.L. Ackerman, R.J. Sternberg, & R. Glaser, eds.). New York: W.H. Freeman 1989

    Google Scholar 

  19. Singley, K.: The reification of goal structures in a calculus tutor: Effects on problem-solving performance. Interactive Learning Environments,1, pp. 102–123 (1990)

    Article  Google Scholar 

  20. Slovin, T., & Woolf, B. P.: A. consultant tutor for personal development. In: Proceedings of the international conference on intelligent tutoring systems (C. Frasson, ed.). Dept. of Information, University of Montreal, Canada 1988

    Google Scholar 

  21. Stevens, A., & Collins, A.: The goal structure of a Socratic tutor. Proceedings of the Association for Computing Machinery Annual Conference (also available as BBN Report No. 3518 from Bolt Beranek and Newman Inc., Cambridge, Mass., 02138) 1977

    Google Scholar 

  22. Suthers, D.: The epistemological structure of explanation. In: Proceedings of AAAI 1990 Workshop on Explanation, Boston, July 1990

    Google Scholar 

  23. Suthers, D., & Woolf, B.: Accounting for the epistemological structure of explanation. In: Working notes of the AAAI 1990 Spring Symposium on Knowledge-based Environments for Learning and Teaching. AAAI, Menlo Park, CA, pp. 46–51. Also available as Technical Report 90–36, Computer and Information Science Dept., University of Massachusetts, Amherst, MA 1990

    Google Scholar 

  24. Wenger, E.: Artificial intelligence and tutoring systems. Los Altos, CA: Morgan Kaufmann 1988

    Google Scholar 

  25. Woods, W.: Transition network grammars for natural language analysis. Communications of the ACM, 13 (10), pp. 591–606 (1970)

    Article  MATH  Google Scholar 

  26. Woolf, B.: Intelligent tutoring systems: A survey. In: Exploring artificial intelligence (H. Shrobe & the American Association for Artificial Intelligence, eds.). Palo Alto, CA: Morgan Kaufmann 1988

    Google Scholar 

  27. Woolf, B., & Cunningham, P.: Multiple knowledge sources in intelligent tutoring systems. IEEE Expert, Los Alamitos, CA, pp 41–54, Summer 1987

    Google Scholar 

  28. Woolf, B., & Murray, T.: A framework for representing tutorial discourse. International Joint Conference in Artificial Intelligence (IJCAI-87), Palo Alto, CA: Morgan Kaufmann, pp. 189–192 (1987)

    Google Scholar 

  29. Woolf, B., Murray, T., Suthers, D., & Schultz, K.: Knowledge primitives for tutoring systems. Proceedings of the International Conference on Intelligent Tutoring Systems (ITS88), University of Montreal, Canada, pp. 491–498 (1988)

    Google Scholar 

  30. Woolf, B., Solway, E., Clancey, W.J., VanLehn, K., & Suthers, D.: Knowledge-based environments for teaching and learning. AI Magazine, 11,5, pp. 74–77 (1991)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Information Science, University of Massachusetts, Amherst, MA, USA

    Beverly Park Woolf

Authors
  1. Beverly Park Woolf
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Advanced Computing and Engineering, Alberta Research Council, 68158 St. N.E., T2E 7H7, Calgary, Alberta, Canada

    Marlene Jones

  2. Faculty of Education, Simon Fraser University, V5A 1S6, Burnaby, British Columbia, Canada

    Philip H. Winne

Rights and permissions

Reprints and permissions

Copyright information

© 1992 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Woolf, B.P. (1992). Towards a Computational Model of Tutoring. In: Jones, M., Winne, P.H. (eds) Adaptive Learning Environments. NATO ASI Series, vol 85. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77512-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-77512-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-77514-7

  • Online ISBN: 978-3-642-77512-3

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation