A knowledge acquisition tool for intelligent computer tutors

ABSTRACT

This research addresses the widening gap between research in intelligent tutoring systems and practical use of this technology by the educational community. In order to insure that intelligent tutoring systems (ITSs) are effective, teachers must be involved in their design and evaluation. We have followed a user participatory design process to build a set of ITS knowledge acquisition tools tailored for usability by teachers. The system facilitates rapid prototyping and testing of curriculum and multiple tutoring strategies. Teachers use the system to create, modify, and test the system's domain content and tutoring strategy knowledge bases. The design includes novel methodologies for strategy representation and overlay student modeling, and incorporates considerations from instructional design theory. Tools have been designed to provide the user with visual models of the concepts and structures of the underlying framework. In close collaboration with a veteran high school teacher, we have used the interface to design a tutor for statics (part of a high school physics course). In this paper we describe the system (called KAFITS), report on our experience involving educators in ITS development, discuss issues of ITS knowledge representation and acquisition, and compare the system with related research in generic tutoring systems and knowledge acquisition.

REFERENCES

• 1: Jeffrey Bonar , Robert Cunningham , Jamie Schultz, An object-oriented architecture for intelligent tutoring systems, Conference proceedings on Object-oriented programming systems, languages and applications, p.269-276, September 29-October 02, 1986, Portland, Oregon, United States
• 2: Breuker, J., Winkels, R., &amp; Sandberg, J. (1987). A Shell for Intelligent Help Systems. In <i>IJCAI-87 Proceedings.</i>
• 3: Jeanette L. Blomberg , Austin Henderson, Reflections on participatory design: lessons from the trillium experience, Proceedings of the SIGCHI conference on Human factors in computing systems: Empowering people, p.353-360, April 01-05, 1990, Seattle, Washington, United States [doi>10.1145/97243.97307]
• 4: Clancey, W.J. (1982). Tutoring Rules for Guiding a Case Method Dialogue. In <i>Intelligent Tutoring Systems,</i> Sleeman and Brown (Eds.). Academic Press, New York.
• 5: William J Clancey, Qualitative student models, Annual review of computer science vol. 1, 1986, Annual Reviews Inc., Palo Alto, CA, 1986
• 6: Clancey, W. &amp; Joerger J (1988). A Practical Authoring Shell for Apprenticeship Learning. In the <i>ITS-88 Proceedings,</i> University of Montreal, June 1988.
• 7: Cohen, P. &amp; Gruber, T. (1985). Reasoning About Uncertainty, A Knowledge Representation Perspective. COINS Tech. report 85--24, University of Massachusetts.
• 8: Duckworth, E., Kelley, J., &amp; Wilson, S. (1987). AI Goes to School. In <i>Academic Computing,</i> November, 1987.
• 9: Halff, H. (1988). Curriculum and Instruction in Automated Tutors. In <i>Foundations of Intelligent Tutoring Systems,</i> Polson &amp; Richardson (Eds.). Lawrence Erlbaum Asso., Hillsdale, NJ.
• 10: Robert R. Hoffman, A survey of methods for eliciting the knowledge of experts, ACM SIGART Bulletin [doi>10.1145/63266.63269]
• 11: Thomas R. Gruber, A Method for Acquiring Strategic Knowledge, University of Massachusetts, Amherst, MA, 1988
• 12: A. Lesgold, Toward a theory of curriculum for use in designing intelligent instructional systems, Learning Issues for Intelligent Tutoring Systems, Springer-Verlag New York, Inc., New York, NY, 1988
• 13: Lesser, V. (1984). Control in Complex Knowledge-based Systems. Tutorial at the IEEE Computer Society AI Conference.
• 14: Macmillan, S., Emme, D., &amp; Berkowitz, M. (1988). "Instructional Planners, Lessons Learned." In <i>Intelligent Tutoring Systems, Lessons Learned,</i> Psotka, Massey, &amp; Mutter (Eds.), Lawrence Erlbaum Assoc., Hillsdale, NJ.
• 15: D. McDonald , J. Brooks , B. Woolf , P. Werner, Transition Networks for Discourse Management, University of Massachusetts, Amherst, MA, 1986
• 16: Merrill, M. D. (1983). Component Display Theory. In <i>Instructional Design Theories and Models,</i> Reigeluth (Ed.). Lawrence Erlbaum Asso., Hillsdale, NJ.
• 17: Merrill, M. D. (1989). An Instructional Design Expert System. <i>Computer-Based Instruction,</i> Vol. 16, No. 3, pp. 95--101.
• 18: Murray, T. (University of Massachusetts Doctoral Dissertation in progress). <i>A Knowledge Acquisition Framework Facilitating Multiple Strategies in an Intelligent Tutor.</i>
• 19: Reigeluth, C. (1983a). Instructional Design: What is it and Why is it? In <i>Instructional Design Theories and Models,</i> Reigeluth (Ed.). Lawrence Erlbaum Asso., Hillsdale, NJ.
• 20: Russell, D. (1988). IDE: The Interpreter. In <i>Intelligent Tutoring Systems, Lessons Learned,</i> Psotka, Massey, &amp; Mutter (Eds.), Lawrence Erlbaum, Assoc.
• 21: Russel, D. M., Moran, T. P., &amp; Jordan, D. S. (1988). The Instructional Design Environment. In Psotka, Massery &amp; Mutter (Eds.) <i>Intelligent Tutoring Systems: Lessons Learned.</i> Lawrence Erlbaum Asso., Hillsdale, NJ.
• 22: Sacerdoti, E., (1974). Planning in a Hierarchy of Abstraction Spaces, in <i>Artificial Intelligence,</i> Vol. 5:2, pp. 115--135, 1974.
• 23: Nigel Shadbolt , A. Mike Burton, The empirical study of knowledge elicitation techniques, ACM SIGART Bulletin [doi>10.1145/63266.63268]
• 24: Van Heuvelen, A. (1987). A Hierarchical Spiral Method of Teaching Physics. Presented at the Summer Meeting of the American Asso. of Physics Teachers, Bozeman, MT, June 17, 1987.
• 25: W. A. Woods, Transition network grammars for natural language analysis, Communications of the ACM, v.13 n.10, p.591-606, Oct. 1970 [doi>10.1145/355598.362773]
• 26: Beverly Park Woolf, Context dependent planning in a machine tutor (artificial intelligence, teaching systems, meno-tutor), 1984
• 27: Woolf, B. &amp; Murray, T. (1987). A Framework for Representing Tutorial Discourse. In <i>IJCAI-87 Proceedings,</i> pp. 189--192.
• 28: Woolf, B., Murray, T., Suthers, D., &amp; Schultz, K. (1988). Knowledge Primitives for Tutoring Systems. In <i>ITS-88 Proceedings,</i> Montreal, Canada, June, 1988, pp 491--498.

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning
          Subjects: Knowledge acquisition

Additional Classification:
  K. Computing Milieux
  K.3 COMPUTERS AND EDUCATION
      K.3.1 Computer Uses in Education

General Terms:
Design, Human Factors, Management, Performance, Theory