Abstract
In a rapidly changing world, where knowledge grows with frightening speed, there is a need for people to be able to analyze and solve problems rather than memorise facts. The complexities of today’s problems are just not amenable to the simplistic approaches so often used in schools at all levels. A result is that great pressures are being brought to bear on educators to change their approaches to instruction. This chapter describes an approach that is designed to help them. It is an approach that interfaces some AI techniques with current classroom needs and existing technology to produce a rich learning environment. Specifically, we draw upon the common observation of people involved in the construction of knowledge bases and expert systems that they themselves gain expertise in the subject matter. To turn this into an instructional benefit, we have students construct simple knowledge bases on difficult topics as a means of forcing them to think deeply about the intrinsic relationships of the topic. They then implement these knowledge bases using a simple expert system shell as a way of testing the projected relationships.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, J.R. (1981). Cognitive skills and their acquisition. Hillsdale, NJ: Lawrence Eribaum.
Armbruster, B.B., & Anderson, T.H. (1984). Mapping: Representing informative text diagrammatically. In C. D. Holley and D. I. Danseieau (Eds.), Spatial learning strategies: Techniques, applications and related Issues. New York Academic Press.
Champagne, A.B., Klopfer, L.E., Desena, A.T., & Squires, D.A. (1981). Structural representations of students’ knowledge before and after science instruction. Journal of Research in Science Teaching, 18, 97–111.
Chi, M, T, H., Feltovich, P.J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121–152.
Derry, S.J., & Murphy, D.A. (1986). Designing systems that train learning ability from theory to practice. Review of Educational Research, 56,1–39.
Duda, R.O., & Shortliffe, E.H. (1983). Expert systems research. Science, 220,261–268.
Field, A. (1982). Getting started. In Jonassen, D.H. (Ed.) The technology of text Educational Englewoodcliffs, N.J.: Technology Publications.
Gagne, E.D. (1977). The conditions of learning (3rd ed.) New York: HolL
Good, R. (1984). Scientific problem solving by expert systems. Journal of Research in Science Teaching, 21, 331–340.
Good, R. (1987). Artificial intelligence and science education. Journal of Research in Science Teaching, 24,325–342.
Greeno, J.G. (1980). Trends in the theory of knowledge for problem solving. In D.T. Tuma and F. Reif (Eds.), Problem solving and education: Issues in teaching and research. Hillsdale, NJ: Lawrence Erlbaum.
Holley, CD., & Dansereau, D.F. (1984a). The Development of Spatial Learning Strategies. In CD. Holley and D.F. Dansereau (Eds.), Spatial learning strategies: Techniques, applications, and related issues. New York: Academic Press.
Holley, CD., & Dansereau, D.F. (Eds.). (1984b). Spatial learning strategies: Techniques, applications and related issues. New York: Academic Press.
Johnson, P.E. (1986). Cognitive models of expertise (Technical Report). Symposium on Expert Systems and Auditor Judgement, University of Southern California, February.
Larkin, J., McDermott, J., Simon, D.P., & Simon, H.A. (1980). Expert and novice performance in solving physics problems. Science, 208,1335–1342.
Larkin, J. H. (1980). Teaching problem solving in physics: The psychological laboratory and the practical classroom. In D.T. Tuma and F. Reif (Eds.), Problem solving and education: Issues in teaching and research. Hillsdale, NJ: Lawrence Erlbaum.
Larkin, J. H., & Rainard, B. (1984). A research methodology to study how people think. Journal of Research in Science Teaching, 21,235–254.
Lippert, R. C. (1987a). Selecting expert system shells for classroom use: Some criteria and guidelines. Computers in Human Behaviour, 3,407–413.
Lippert, R. C. (1987b). Teaching problem solving in mathematics and science with expert systems. School Science and Mathematics, 87,477–493.
Lippert, R. C (1987c). Development of expert systems: An instructional strategy for dealing with misconceptions. Proceedings of the Second International Conference on Misconceptions in Science and Mathematics, Vol. 1. Cornell University, Ithaca, NY.
Mirande, M. J. A. (1984). Schematizing: Technique and applications. In CD. Holley and D.F. Dansereau (Eds.), Spatial learning strategies: Techniques, applications, and related issues. New York: Academic Press.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall. Novak, J. D., & Gowin, D.B. (1984). Learning how to learn. Cambridge, UK: Cambridge University Press.
Shavelson, R. (1974). Methods for examining representations of a subject matter structure in a student’s memory. Journal of Research in Science Teaching, 11,231–249.
Simon, D. P., & Simon, H. L. (1977). A tale of two protocols. In J. Lochhead and J. Clement (Eds.), Cognitive process instruction: Research on teaching and thinking skill. Philadelphia, PA: Franklin Institute Press.
Starfield, A. M., Butala, K. L., England, M. L., & Smith, K. A. (1983). Mastering engineering concepts by building an expert system. Engineering Education, 74(2), 104–107.
Starfield, A. M., & Bleloch, A. L. (1983). Expert systems: An approach to problems in ecological management that are difficult to quantify. International Journal of Environmental Management, 16,261–268.
Starfield, A. M, Adams, S.R., & Bleloch, A.L. (1985). A Small Expert System Shell and its Applications. Proceedings of the 4th International Conference on Computers and Communications, 262–267. Los Alamitos, CA: IEEE Computer Science Press.
Starfield, A. M., Smith, K. A., & Bleloch, A. L. (1990). How to model it: Problem solving for the computer age. New York: McGraw Hill.
Sternberg, R. J. (1985). Instrumental and Componential Approaches to the Nature and Training of Intelligence. In S. F. Chipman, J. W. Segal, and K. Glaser (Eds.), Thinking and learning skills, Volume II. Hillsdale, NJ: Lawrence Erlbaum.
Stewart, J. (1985). Cognitive science and science education. European Journal of Science Education, 7,1–17.
Trollip, S. R., & Lippert, R. C. (1987). Constructing knowledge bases: A promising instructional tool. Journal of Computer- Based Instruction, 14,44–48.
Trollip, S. R., & Lippert, R. C. (1989). Constructing knowledge bases: A Process for Instruction. In P. A. Hancock and M. H. Chignell (Eds), Intelligent Interfaces: Theory, Research and Design. North-Holland: Elsevier Science Publishers B.V.
Vosniadou, S., & Brewer, W. F. (1987). Theories of knowledge restructuring in development. Review of Educational Research, 57, 51–67.
West, L. H. T. (1985). Concept Mapping. Paper presented at the Chicago, IL Meeting of the American Educational Research Association.
Wilkins, D. C, Buchanan, B. G., & Clancey, W. G. (1984). Inferring an expert’s reasoning by watching (Technical Report) Stanford University, Department of Computer Science, Heuristic Programming Project, HPP-84–29.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1992 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Trollip, S.R., Lippert, R.C., Starfield, A.M., Smith, K.A. (1992). Building Knowledge Bases: An Environment for Making Cognitive Connections. In: Kommers, P.A.M., Jonassen, D.H., Mayes, J.T., Ferreira, A. (eds) Cognitive Tools for Learning. NATO ASI Series, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-77222-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-77222-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-77224-5
Online ISBN: 978-3-642-77222-1
eBook Packages: Springer Book Archive