Answering student queries: Functionality and mechanisms

  • Daniel D. Suthers
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)


This paper discusses the design of an explanation facility for answering student-initiated questions. The facility is based on a hybrid planning architecture that models functionally distinct subtasks of explanation planning with mechanisms suited for those tasks. The paper focuses on mechanisms for four of these tasks: selection of knowledge relevant to a query, choice of conceptual model on which to base the explanation, addition of pedagogically motivated material, and coherent ordering. Specific recommendations are made for the design of an ITS capable of providing these functionalities when responding to student initiated queries.


Task Group Capacitor Store Plan Critic Refinement Operator Graph Traversal 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    A. Cawsey: Generating interactive explanations. Proc 9th AAAI. Anaheim, California, 1991.Google Scholar
  2. 2.
    B. Falkenhainer & K. Forbus: Compositional Modeling: Finding the right model for the job. Artificial Intelligence. 51. 1991.Google Scholar
  3. 3.
    E. Hovy: Pragmatics and natural language generation. Artificial Intelligence. 43(2), 1991.Google Scholar
  4. 4.
    J. Lester & P. Porter: A revision-based model of instructional multi-paragraph discourse production. Proc. 13th Cognitive Science. Chicago, 1991.Google Scholar
  5. 5.
    M. Maybury: Planning multisentential English text using communicative acts. Unpublished doctoral dissertation, Cambridge University, 1991.Google Scholar
  6. 6.
    K. McCoy: Generating context-sensitive responses to object-related misconceptions. Artificial Intelligence. 41(2), 1989.Google Scholar
  7. 7.
    K. McKeown, M. Wish & K. Matthews: Tailoring explanations for the user. Proc. 9th IJCAI. Los Angeles, California, 1985.Google Scholar
  8. 8.
    J. Moore & W. Swartout: A Reactive Approach to Explanation. Proc. 11th IJCAI. Detroit, Michigan, 1989.Google Scholar
  9. 9.
    T. Murray & B. Woolf: A Knowledge Acquisition Framework for Intelligent Learning Environments. Proc. ITS-92 (this volume).Google Scholar
  10. 10.
    C. Paris. Combining discourse strategies to generate descriptions to users along a Naive/Expert spectrum. Proc. 10th IJCAI, Milan, Italy, 1987.Google Scholar
  11. 11.
    E. Rissland (Michener): Understanding understanding mathematics. Cognitive Science. 2(4), 1978.Google Scholar
  12. 12.
    J. Self: Bypassing the intractable problem of student modeling. Proc. ITS-88. Monteal, 1988.Google Scholar
  13. 13.
    D. Suthers: Providing multiple views of reasoning for explanation. Proc. ITS-88. Montreal, 1988.Google Scholar
  14. 14.
    -: A task-appropriate hybrid architecture for explanation. Computational Intelligence. 7(4), 1991.Google Scholar
  15. 15.
    -: An analysis of explanation and implications for the design of explanation planners. Ph.D. dissertation, University of Massachusetts, Amherst, 1992.Google Scholar
  16. 16.
    D. Suthers, B. Woolf, & M. Cornell: Steps from explanation planning to model construction dialogues. Proc. 10th AAAI. San Jose, California, 1992.Google Scholar
  17. 17.
    B. White & J. Frederiksen: Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence. 42(1), 1990.Google Scholar
  18. 18.
    I. Zukerman: A predictive approach for the generation of rhetorical devices. Computational Intelligence. 6(1), 1990.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Daniel D. Suthers
    • 1
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherst

Personalised recommendations