Abstract
Starting out from Anderson’s concept of procedural learning and White and Frederiksen’s approach of mental models, student modelling in the domain of intelligent tutoring systems is discussed with regard to cognitive approaches. Anderson’s approach provides a conceptual basis for analyzing knowledge acquisition processes with regard to cognitive skills. Within the framework of this approach Anderson models problem-solving behaviour and knowledge acquisition processes based on the description of an ideal student behaviour on the one hand (expert model), and incorrect behaviour on the other hand (bug library), applying production rules. White and Frederiksen focus primarily on conceptual knowledge and relate to the construct of mental models. Knowledge acquisition processes are modeled as a progression of mental models which the student passes through in the course of acquiring expertise. A more critical perspective reveals that Anderson lays emphasis on procedural learning, completely neglecting cognitive processes. The weak points of White and Frederiksen’s approach consist in the fact that knowledge acquisition processes are not described in psychological terms, but rather in physical terms. Although the above-mentioned ways of approaching the matter of knowledge acquisition differ widely, cognitive approaches allow a more differentiated analysis of the representation and acquisition of knowledge, thus providing an important basis for student modelling.
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References
Anderson, J.R.: Language, memory and thought Hillsdale, NJ.: Erlbaum 1976.
Anderson, JJt.: The architecture of cognition. Cambridge, M.A: Harvard University Press 1983.
Anderson, Jit.: A theory of the origins of knowledge. Artificial Intelligence, 40,313–351,1989.
Anderson, JH.: Analysis of Student Performance with the LISP Tutor. In N. Frederiksen, R. Glaser, A. Lesgold & M.G. Shafto (Eds.), Diagnostic Monitoring of Skill and Knowledge Acquisition (pp. 27–50). Hillsdale, N. J.: Erlbaum, 1990.
Anderson, J JR. & Reiser, B J.: The LISP tutor. Byte, 10 (4), 159–175,1985.
Anderson, J.R., Boyle, C. & Yost, G.: The geometry tutor. Proceedings of Ninth International Joint Conference on Artificial Intelligence (pp. 1–7). Los Altos, CA: Morgan Kaufmann, 1985.
Anderson, JJR., Farrel, R. & Sauers, R.: Learning to program in LISP. Cognitive Science, 8, 87–129,1984.
Bobrow, D.G. (Ed.): Artificial Intelligence. Special volume on qualitative reasoning about physical systems. Amsterdam: North-Holland, 1984.
Brown, J.S. & Burton, RJR. : Diagnostic models for procedural bugs in mathematical skills. Cognitive Science, 2,155–191,1978.
Brown, J.S., Burton, R.R. & de Kleer, J.: Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II and in. In D. Sleeman & J.S. Brown (Eds.), Intelligent Tutoring Systems (pp. 227–282). New York: Academic Press, 1982.
Burton, R.: Diagnosing bugs in a simple procedural skill In D. Sleeman & J.S. Brown (Eds.), Intelligent Tutoring Systems (pp. 157–184). New York: Academic Press, 1982.
Carbonell, J.G.: AI in CAÍ: an artificial intelligence approach to computer-assisted instruction. IEEE Trans. Man-Mach. Syst, 11, 1970.
Collins, A. & Quillian, MJR.: Retrieval time from semantic memory. Journal of Verbal Learning and Verbal Behavior, 6,240–247,1969.
Craik, K.: The nature of explanation. Cambridge: Cambridge University Press, 1943.
De Kleer, J. & Brown, J.S.: Assumptions and ambiguities in mechanistic mental models. In D. Centner & AL. Stevens (Eds.), Mental models (pp. 155–190). Hillsdale, NJ.: Lawrence Erlbaum, 1983.
Forbus, K.D. & Stevens, AX.: Using qualitative simulation to generate explanations. Report No. 4490. Cambridge, MA: BBN Laboratories, 1981.
Goldstein, I.: The genetic graph: a representation for the evolution of procedural knowledge. In D. Sleeman & J.S. Brown (Eds.), Intelligent tutoring systems (pp. 51–77). New York: Academic Press, 1982.
Johnson-Laird, P.N.: Mental models. Cambridge: Cambridge University Press, 1983.
Kluwe, R.H. & Haider, H.: Modelle zur internen Repräsentation komplexer technischer Systeme. Sprache & Kognition, 9, 173–192, 1990.
Langley, P., Ohlsson, S. & Sage, S.:. Machine learning approach to student modelling. Technical Report CMU-RI-TR-84–7. Pittsburgh, PA: Robotics Institute, Carnegie-Mellon University, 1984.
Mandl, H. & Lesgold, A. (Eds.): Learning issues for intelligent tutoring systems. New York: Springer, 1988.
Ohlsson, S. & Langley, P.: Psychological evaluation of path hypothesis in cognitive diagnosis. In H. Mandl & A. Lesgold (Eds.), Learning issues for intelligent tutoring systems (pp. 42–62). New York: Springer, 1988.
Payne, SJ.: Methods and mental models in theories of cognitive skill. In J. Self (Ed.), Artificial intelligence and human learning (pp. 69–87). London: Champan and Hall, 1988.
Poison, M.C. & Richardson, J J.: Foundations of Intelligent Tutoring Systems. Hillsdale, NJ.: Erlbaum, 1988.
Psotka, J., Massey, L.D. & Mutter, S.A. (Eds.).: Intelligent Tutoring Systems: Lessons learned. Hiüsdale, N J.: Erlbaum, 1988.
Reimann, P.: Towards general knowledge - diagnosis systems for student and user modeling. In H. Mandl, E. de Corte, N. Bennett & H.F. Friedrich (Eds.) Learning and Instruction. European Research in an International Context Volume 2.1. Social and cognitive Aspects of Learning and Instruction (pp. 349–365). Oxford: Pergamon Press, 1990.
Reiser, B.V., Anderson, J.R. & Farrel, R.G.: Dynamic Student modeling in an intelligent tutor for LISP programming. Proceedings of Ninth International Joint Conference of Artificial Intelligence (pp. 8–14). Los Altos, CA: Morgan Kaufmann, 1985.
Rumelhart, D.E. & Norman, D.A.: Representation in memory. CHIP Report 116. San Diego: University of California, 1983.
Sauers, R. & Farrel, R.: GRAPES user’s manual, Technical Report 1, Department of Psychology, Carnegie-Mellon University, Pittsburgh, PA, 1982
Sleeman, D.H.: Inferring (mal) rules from pupils’ protocols. Proceedings of the European Conference on Artificial Intelligence (pp. 160–164). Qrsay, France, 1982.
Sleeman, D. & Brown, J.S. (Eds.): Intelligent Tutoring Systems. New York: Academic Press, 1982.
Van Lehn, KL: Student modelling. In M.C. Poison & J J. Richardson (Eds.), Foundations of Intelligent Tutoring Systems (pp. 55–78). Hillsdale, NJ.: Erlbaum, 1988.
White, B.Y. & Frederiksen, J.R.: Progressions of qualitative models as a foundation for intelligent learning environments. Report No. 6277. Cambridge, MA: BBN Laboratories Inc., 1986.
White, B.Y. & Frederiksen, J.R.: Qualitative models and intelligent learning environments. In R.W. Lawler & M. Yazdani (Eds.), Artificial Intelligence and education; Volume 1: Learning environments and tutoring systems (pp. 281–305). Norwood, NJ.: Ablex Publishing, 1987.
White, B.Y. & Frederiksen, J.R.: Causal model progressions as a foundation for intelligent learning environments. In WJ. Clancey & E. Soloway (Eds.), Artificial Intelligence. Special issue on artificial intelligence and learning environments (pp. 99–157). 42,1. Amsterdam: North-Holland, 1990.
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Mandl, H., Hron, A. (1992). Cognitive Theories as a Basis for Student Modelling. In: Tiberghien, A., Mandl, H. (eds) Intelligent Learning Environments and Knowledge Acquisition in Physics. NATO ASI Series, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84784-4_9
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DOI: https://doi.org/10.1007/978-3-642-84784-4_9
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