Advertisement

Towards the theory-guided design of help systems for programming and modelling tasks

  • Claus Möbus
  • Knut Pitschke
  • Olaf Schröder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 608)

Abstract

This paper describes an approach to the design of online help for programming tasks and modelling tasks, based on a theoretical framework of problem solving and learning. The framework leads to several design principles which are important to the problem of when and how to supply help information to a learner who is constructing a solution to a given problem. We will describe two example domains where we apply these design principles: The ABSYNT problem solving monitor supports learners with help and proposals for functional programming. The PETRI-HELP system currently under development is intended to support the learning of modelling with Petri nets.

Keywords

Model Check Design Principle Design Rule Functional Programming Intelligent Tutor System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    J.R. Anderson: The Architecture of Cognition. Cambridge: Harvard University Press, 1983Google Scholar
  2. 2.
    J.R. Anderson: Knowledge Compilation: The General Learning Mechanism. In: R.S. Michalski, J.G. Carbonell, T.M. Mitchell, Machine Learning II. Kaufman, 1986, 289–310Google Scholar
  3. 3.
    J.R. Anderson: Production Systems, Learning, and Tutoring, in D. Klahr, P. Langley, R. Neches (eds): Production System Models of Learning and Development. Cambridge: MIT Press, 1987, 437–458Google Scholar
  4. 4.
    J.R. Anderson: A Theory of the Origins of Human Knowledge, Artificial Intelligence, 1989, 40, 313–351Google Scholar
  5. 5.
    J.R. Anderson, F.G. Conrad, A.T. Corbett: Skill Acquisition and the LISP Tutor, Cognitive Science, 1989, 13, 467–505Google Scholar
  6. 6.
    F.L. Bauer, G. Goos: Informatik, 1. Teil, Berlin: Springer, 1982 (third ed.)Google Scholar
  7. 7.
    J.S. Brown, K. van Lehn: Repair Theory: A Generative Theory of Bugs in Procedural Skills. Cognitive Science, 1980, 4, 379–426Google Scholar
  8. 8.
    M.T.H. Chi, M. Bassok, M.W. Lewis, P. Reimann, R. Glaser: Self-Explanations: How Students Study and Use Examples in Learning to Solve Problems, Cognitive Science, 1989, 13, 145–182Google Scholar
  9. 9.
    E.M. Clarke, F.A. Emerson, A.P. Sistla: Automatic Verification of Finite-State Concurrent Systems Using Temporal Logic Specifications. ACM Transactions on Programming Languages and Systems, 1986, Vol. 8, No. 2, 244–263Google Scholar
  10. 10.
    G.W. Ernst, A. Newell: GPS: A Case Study in Generality and Problem Solving, New York: Academic Press, 1969Google Scholar
  11. 11.
    P.M. Gollwitzer: Action Phases and Mind-Sets, in: E.T. Higgins, R.M. Sorrentino (eds), Handbook of Motivation and Cognition, 1990, Vol.2, 53–92Google Scholar
  12. 12.
    H. Heckhausen: Motivation und Handeln, Heidelberg: Springer, 1989 (second ed.)Google Scholar
  13. 13.
    B. Josko: Verifying the Correctness of AADL Modules using Model Checking. In: de Bakker, de Roever, Rozenberg (eds): Proceedings REX-Workshop on Stepwise Refinement of Distributed Systems: Models, Formalisms, Correctness. Springer LNCS 430, 1990Google Scholar
  14. 14.
    J.E. Laird, P.S. Rosenbloom, A. Newell: Universal Subgoaling and Chunking. The Automatic Generation and Learning of Goal Hierarchies, Boston: Kluwer, 1986Google Scholar
  15. 15.
    J.E. Laird, P.S. Rosenbloom, A. Newell: SOAR: An Architecture for General Intelligence, Artificial Intelligence, 1987, 33, 1–64Google Scholar
  16. 16.
    C. Lewis: Composition of Productions, in D. Klahr, P. Langley, R. Neches (eds), Production System Models of Learning and Development. Cambridge: MET Press, 1987, 329–358Google Scholar
  17. 17.
    C. Möbus: The Relevance of Computational Models of Knowledge Acquisition for the Design of Helps in the Problem Solving Monitor ABSYNT, in R.Lewis, S.Otsuki (eds), Advanced Research on Computers in Education, IFIP TC3, North-Holland, 1991, 137–144Google Scholar
  18. 18.
    C. Möbus, K. Pitschke, O. Schröder: Ein wissensstandsbezogenes Hilfesystem für Petrinetzmodellierer, in: V. Claus, U. Lichtblau (eds): 2. Kolloquium der Arbeitsgruppe Informatiksysteme, Bericht AIS-3, Universität Oldenburg, 1991Google Scholar
  19. 19.
    C. Möbus, O. Schröder: Representing Semantic Knowledge with 2-dimensional Rules in the Domain of Functional Programming, in: P.Gorny, M. Tauber (eds), Visualization in Human-Computer Interaction, Springer, 1990 (LNCS 439), 47–81Google Scholar
  20. 20.
    C. Möbus, O. Schröder, H.-J. Thole: Runtime Modeling the Novice-Expert Shift in Programming Skills on a Rule-Schema-Case Continuum, in: J. Kay; A. Quilici (eds), Proc IJCAI Workshop W.4 Agent Modelling for Intelligent Interaction, 1991, 137–143Google Scholar
  21. 21.
    C. Möbus, H.-J. Thole: Interactive Support for Planning Visual Programs in the Problem Solving Monitor ABSYNT: Giving Feedback to User Hypotheses on the Basis of a Goals-Means-Relation, in: D.H. Norrie, H.-W. Six (eds), Proc. 3rd Int. Conf on Computer-Assisted Learning ICCAL 90, Heidelberg: Springer, 1990 (LNCS 438), 36–49Google Scholar
  22. 22.
    D.M. Neves, J.R. Anderson: Knowledge Compilation: Mechanisms for the Automatization of, Cognitive Skills, in J.R. Anderson (ed), Cognitive Skills and their Acquisition. Hillsdale, Erlbaum, 1981, 57–84Google Scholar
  23. 23.
    O. Schröder: A Model of the Acquisition of Rule Knowledge with Visual Helps: The Operational Knowledge for a Functional Visual Programming Language, in: D.H. Norrie, H.-W. Six (eds), ICCAL 90, Heidelberg: Springer, 1990 (LNCS 438), 142–157Google Scholar
  24. 24.
    J.A. Self: Bypassing the Intractable Problem of Student Modelling, in C. Frasson, G. Gauthier (eds), Intelligent Tutoring Systems, Norwood: Ablex, 1990, 107–123Google Scholar
  25. 25.
    D. Sleeman, J.S. Brown (eds), Intelligent Tutoring Systems, New York: Acad Press, 1982Google Scholar
  26. 26.
    K. van Lehn: Toward a Theory of Impasse-Driven Learning, in H. Mandl, A. Lesgold (eds), Learning Issues for Intelligent Tutoring Systems. New York: Springer, 1988, 19–41Google Scholar
  27. 27.
    K. van Lehn: Mind Bugs: The Origins of Procedural Misconceptions, MIT Press, 1990Google Scholar
  28. 28.
    K. van Lehn: Rule Acquisition Events in the Discovery of Problem-Solving Strategies, Cognitive Science, 1991, 15, 1–47Google Scholar
  29. 29.
    K. van Lehn:, R.M. Jones, M.T.H Chi: Modelling the Self-Explanation Effect with Cascade 3, Learning Research and Development Center, University of Pittsburgh, 1991Google Scholar
  30. 30.
    S.A. Vere: Relational Production Systems, Artificial Intelligence, 1977, 8, 47–68Google Scholar
  31. 31.
    E. Wenger: Artificial Intelligence and Tutoring Systems, Los Altos: Kaufman, 1987Google Scholar
  32. 32.
    R. Winkels, J. Breuker: Discourse Planning in Intelligent Help Systems, in: C. Frasson, G. Gauthier (eds), Intelligent Tutoring Systems, Norwood: Ablex, 1990, 124–139Google Scholar
  33. 33.
    J.G. Wolff: Cognitive Development as Optimisation, in L. Bolc (ed), Computational Models of Learning. Berlin: Springer, 1987, 161–205Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Claus Möbus
    • 1
  • Knut Pitschke
    • 1
  • Olaf Schröder
    • 1
  1. 1.Dept. of Computational Science Unit on Tutoring and Learning SystemsUniversity of OldenburgOldenburgGermany

Personalised recommendations