Abstract
From a psychological point of view efficient teaching by means of an intelligent tutoring system necessarily involves that the communication of knowledge is adapted to the requirements of the learner: to her cognitive abilities, her pre-instructional knowledge and her learning capabilities. To tackle these topics in a precise way, we have developed the artificial-intelligence-based microworld DiBi (disk billiard) and MULEDS, a multi-level diagnosis system. The microworld DiBi sets up a learning environment which simulates elastic impacts as a subtopic of classical mechanics. DiBi enables and supports reasoning on different levels of mental domain representation ordered along the dimension ‘qualitative/quantitative’. This way of representing the domain provides a basis for passive adaptation in an advanced way. Correspondingly, active adaptation is supported by MULEDS, wherein student modeling is realized by assessing the student’s correct and/or incorrect domain-specific knowledge at these different levels. Within this psychological perspective, the use of instructional tools, such as the microworld DiBi and the computerized diagnosis system MULEDS, aims at gradually supporting and guiding the student in the construction of more and more powerful an sound domain representations. The progression through these levels of domain representation will enable the student to solve the problems posed by the domain in a flexible way.
Résumé
D’un point de vue psychologique, un enseignement efficace avec un tutoriel intelligent implique nécessairement que la connaissance à communiquer soit adaptée aux besoins de l’élève: ses aptitudes cognitives, ses connaissances préalables et sa capacité d’apprentissage. Pour aborder ces problèmes, nous avons développé le système DiBi (disk billiard) — un micro-monde basé sur l’Intelligence Artificielle — et MULEDS (multi-level diagnosis system), un système de diagnostic à plusieurs niveaux. Le micro-monde DiBi présente un environnement d’apprentissage simulant des chocs élastiques en mécanique classique. DiBi facilite le raisonnement à plusieurs niveaux de représentation mentale caractérisés selon la dimension «qualitatif/quantitatif». Le système MULEDS assure l’adaptation de l’environnement aux réponses de l’élève. Celui-ci est modélisé en fonction des connaissances spécifiques — correctes ou incorrectes — correspondant aux différents niveaux. L’utilisation d’outils d’enseignements comme DiBi ou MULEDS vise à soutenir et à guider l’élève, dans la construction de représentations de plus en plus élaborées et puissantes. La progression à travers les différents niveaux de représentation devrait permettre d’accroître la flexibilité dans la résolution de problèmes.
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References
Anderson, J. R., & Skwarecki, E. (1986). The automated tutoring of introductory computer programming.Communications of the A.C.M., 29, 842–849.
Anderson, J.R. (1988). The expert module. In M. C. Polson & J. J. Richardson (Eds.),Foundations of intelligent tutoring systems (pp. 21–53). Hillsdale, NJ: Lawrence Erlbaum.
Brown, J. S., & Burton, R. R. (1978). Diagnostic models for procedural bugs in basic mathematical skills.Cognitive Science, 2, 155–191.
Brown, J. S., Burton, R. R., & DeKleer, J. (1982). Pedagogical, natural language, and knowledge engineering techniques in SOPHIE I, II, and III. In D. H. Sleeman & J. S. Brown (Eds.),Intelligent tutoring systems (pp. 227–282) London: Academic Press.
Carey, S. (1986). Cognitive science and science education.American Psychologist, 41, 1123–1130.
Champagne, A. B., Klopfer, L. E., & Anderson, J. H. (1980). Factors influencing the learning of classical mechanics.American Journal of Physics, 48, 1074–1079.
Chi, M. T. H., Glaser, R. & Farr, M. J. (Eds.), (1988).The nature of expertise. Hillsdale, N.J.: Lawrence Erlbaum.
Clancey, W. J. (1986). Qualitative student models.Annual Review of Computer Science, 1, 381–450.
Clancey, W. J. (1987).Knowledge-based tutoring: The GUIDON program. Cambridge, M.A.: M.I.T. Press.
Evertsz, R. (1989). Refining the student’s procedural knowledge through abstract interpretations. In D. Bierman, J. Breuker, & J. Sandberg (Eds.),Proceedings of the 4th International Conference on AI and Education (pp. 101–106). Amsterdam: I.O.S.
Hutchins, E. L., Hollan, J. D., & Norman, D. A. (1986). Direct manipulation interfaces. In D. A. Norman & S. W. Draper (Eds.),User centered system design (pp. 87–124). Hillsdale, N.J.: Lawrence Erlbaum.
Klahr, D., & Siegler, R. (1978). The representation of children’s knowledge. In H. W. Reese & L. P. Lipsett (Eds.),Advances in child development. N.Y.: Academic Press.
Larkin, J. H. (1983). The role of problem representation in physics. In D. Gentner & A. Stevens (Eds.),Mental models (pp. 75–98). Hillsdale, N.J.: Lawrence Erlbaum.
Ohlsson, S., & Langley, P. (1984). P.R.I.S.M.:Tutorial, manual, and documentation (Technical Report). Pittsburgh, P.A.: The Robotics Institute, Carnegie-Mellon University.
Ohlsson, S., & Rees, E. (1988).An information processing analysis of the function of conceptual understanding in the learning of arithmetic procedures (Technical Report No KUL-88-03) Pittsburgh, P.A.: Learning Research and Development Center.
Sleeman, D. H., & Smith, M. J. (1981). Modeling student’s problem solving.Artificial Intelligence, 16, 171–187.
Spada, H., Reimann, P. & Haeusler, B. (1983). Hypothesenerarbeitung und Wissensaufbau bei Schuelern. In L. Koetter & H. Mandl (Hrsg.),Lehrbuch fuer Empirische Erziehungswissenschaften (S. 139–167). Duesseldorf: Schwann.
Spada, H., Stumpf, M., & Opwis, K. (1989). The constructive process of knowledge acquisition: student modeling. In H. Maurer (Ed.),Proceedings of the 2nd International Conference on Computer-assisted Learning (pp. 486–499) Berlin: Springer.
Stumpf, M., Branskat, S., Herderich, C., Newen, A., Opwis, K., Ploetzner, R., Schult, T., & Spada, H. (1988).The graphical user interface of DiBi, a microworld for collision phenomena (Research Report No 44). Freiburg: Psychological Institute.
Thompson, P. W. (1987). Mathematical microworlds and intelligent computer-assisted instruction. In G. P. Kearsley (Ed.),Artificial intelligence & instruction (pp. 83–110). Reading, M.A.: Addison-Wesley.
VanLehn, K. (1987). Learning one subprocedure per lesson.Artificial Intelligence, 31, 1–40.
Wenger, E. (1987).Artificial intelligence and tutoring systems. Los Altos, C.A.: Morgan Kaufmann.
White, B. Y. (1983). Sources of difficulty in understanding Newtonian dynamics.Cognitive Science, 7, 41–65.
White, B. Y., & Frederiksen, J. R. (1986).Progressions of qualitative models as a foundation for intelligent learning environments. (Technical Report No 6277). B.B.N. Laboratories.
Woolf, B. (1988). Intelligent tutoring systems: A survey. In H. E. Shrobe (Ed.),Exploring artificial intelligence (pp. 1–43). San Mateo, C.A.: Morgan Kaufmann.
Yong, R. M., & O’Shea, T. (1981). Errors in children’s subtraction.Cognitive Science, 5, 153–177.
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The research reported herein was supported by the German Research Foundation (Deutsche Forschungsgemeinschaft), Grant Sp 251/2-x to the second author.
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Ploetzner, R., Spada, H., Stumpf, M. et al. Learning qualitative and quantitative reasoning in a microworld for elastic impacts. Eur J Psychol Educ 5, 501–516 (1990). https://doi.org/10.1007/BF03173135
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DOI: https://doi.org/10.1007/BF03173135