Abstract
Despite some optimistic claims of the contrary it is still in the distant future to teach by means of truly self-adapting systems. Nevertheless, one main focus of Cognitive Science lies on this issue of how to construct a system which has the special feature to adjust its behavior to the student/user in a sophisticated way. One answer is student modeling. Findings of Cognitive Psychology and tools of Artificial Intelligence are combined to assess the student's knowledge and the learning process she is subject to, while working with the system. We discuss several approaches towards student modeling namely overlay models, enumerative diagnosis systems, and generative models based on theories of knowledge acquisition.
To address these topics, we have developed the AI-based microworld DiBi (disk billiard) and FEDS, a flat enumerative diagnosis system. Both systems are implemented on XEROX 11xx/SIEMENS 58xx machines running InterLISP-D and PRISM.
DiBi is a computerized learning environment for elastic impacts as a subtopic of classical mechanics. The student learns by designing experiments, making predictions about their outcomes and by revising her hypotheses based on a comparison of her predictions with the computer-generated feedback. The constructive process of knowledge acquisition can be understood as experience-based learning.
A form of passive adaptation to the student can be seen as realized in two aspects of DiBi: Having all characteristics of a microworld, the system enables the student to access optimally fitting information in a self-guided way. In addition, DiBi supports quantitative and qualitative thinking in several ways.
Active adaptation presupposes some kind of student modeling. By means of FEDS, correct quantitative domain-specific knowledge, but also qualitative knowledge and misconceptions are assessed in form of correct, fragmentary and faulty hypotheses. We are working on an improved diagnosis system which is explicitly based on elements of a theory of knowledge acquisition. As a consequence, we will represent the domain in a way which facilitates knowledge communication between system and student in all phases of the learning process. The long-term objective is to develop a really self-adapting teaching system.
This is a preview of subscription content, log in via an institution.
Preview
Unable to display preview. Download preview PDF.
References
Bobrow, D.G. (Ed.) (1985). Qualitative reasoning about physical systems. Cambridge, MA: MIT Press.
Brown, J.S. & Burton, R.R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–191.
Brown, J.S. & VanLehn, K. (1980). Repair theory: a generative theory of bugs in procedural skills. Cognitive Science, 4, 379–426.
Burton, R.R. (1982). Diagnosing bugs in a simple procedural skill. In D.H. Sleeman & J.S. Brown (Eds.), Intelligent Tutoring Systems (pp. 157–184). London: Academic Press.
Carr, B. & Goldstein, J.P. (1977). Overlays: a theory of modeling for computer-aided instruction. AI Lab Memo 406 (Logo Memo 40). Cambridge, MA: MIT.
Di Sessa, A. (1982). Unlearning Aristotelian physics: a study of knowledge-based learning. Cognitive Science, 6, 37–75.
Falkenhainer, B.C. & Michalski, R.S. (1986). Integrating quantitative and qualitative discovery. The ABACUS system. Machine Learning, 1, 367–402.
Holland, J., Holyoak, K., Nisbett, R.E. & Thagard, P. (1987). Induction: processes of inference, learning and discovery. Cambridge, MA: MIT Press.
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, NJ: Lawrence Erlbaum.
Klahr, D., Langley, P. & Neches, R. (Eds.). (1987). Production system models of learning and development. Cambridge, MA: MIT Press.
Langley, P. (1987). A general theory of discrimination learning. In D. Klahr, P. Langley & R. Neches (Eds.), Production system models of learning and development (pp. 99–161). Cambridge, MA: MIT Press.
Langley, P., Ohlson, S. & Sage, S. (1984). Machine learning approach to student modeling (Technical Report CMU-RI-TR-84-7). Pittsburgh, PA: The Robotics Institute, Carnegie-Mellon University.
Ohlsson, S. & Langley, P. (1984). PRISM: Tutorial, manual, and documentation (Technical Report). Pittsburgh, PA: The Robotics Institute, Carnegie-Mellon University.
Opwis, K. (1988). Produktionssysteme. In H. Mandl & H. Spada (Hrsg.), Wissenspsychologie (S. 74–98). München: Urban & Schwarzenberg.
Opwis, K., Stumpf, M. & Spada, H. (1987) PRISM: Eine Einführung in die Theorie und Anwendung von Produktionssystemen (Research Report No. 39). Freiburg: Psychological Institute.
Plötzner, R. & Opwis, K. (1987). Modeling discrimination learning in a production system framework (Research Report No. 40). Freiburg: Psychological Institute.
Reimann, P. (in press a). Modeling discovery learning processes in a microworld for geometrical optics. Proceedings of the European Summer University on Intelligent Tutoring Systems. Le Mans 1988.
Reimann, P. (in press b). Toward general knowledge diagnosis systems for student and user modeling. In H. Mandl, E. DeCorte, N. Bennett & H.F. Friedrich (Eds.) Learning and instruction. European research in an international context. Vol. II & III. Oxford: Pergamon Press.
Sleeman, D.H. (1982). Infering (mal) rules from pupil's protocols. In Proceedings of the European Conference on Artificial Intelligence. Orsay, 160–164.
Smith, R.B. (1986). The Alternate Reality Kit: an animated environment for creating interactive simulations. Proceedings of the IEEE Computer Society Workshop on Visual Languages. Dallas, 1986, 99–106.
Spada, H. & Reimann, P. (1986). Hypothesis formation in knowledge acquisition: Preparing the ground for an intelligent tutoring system. In F. Klix & H. Hagendorf (Eds.), Human memory and cognitive capabilities (pp. 951–961). Amsterdam: North Holland.
Spada, H. & Reimann, P. (1988). Wissensdiagnostik auf kognitionswissenschaftlicher Basis. Zeitschrift für Differentielle und Diagnostische Psychologie, 3, 183–192.
Stumpf, M., Opwis, K. & Spada, H. (in press). Knowledge acquisition in a microworld for elastic impacts: The DiBi system. Proceedings of the European Summer University on Intelligent Tutoring Systems, Le Mans, 1988.
Stumpf, M., Branskat, S., Herderich, C., Newen, A., Opwis, K., Plötzner, R., Schult, T. & Spada, H. (1988). The graphical user interface of DiBi, a microworld for collision phenomena (Research Report No. 44). Freiburg: Psychological Institute.
VanLehn, K. (1987). Learning one subprocedure per lesson. Artificial Intelligence, 31, 1–40.
Wenger, E. (1987). Artificial intelligence and tutoring systems. Los Altos, CA: Kaufmann.
White, B.Y. & Horwitz, P. (1987). Thinker tools: enabling children to understand physical laws (Report No. 6470). Cambridge, MA: BBN Laboratories.
Young, R.M. & O'Shea, T. (1981). Errors in children's subtraction. Cognitive Science, 5, 153–177.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1989 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Spada, H., Stumpf, M., Opwis, K. (1989). The constructive process of knowledge acquisition: Student modeling. In: Maurer, H. (eds) Computer Assisted Learning. ICCAL 1989. Lecture Notes in Computer Science, vol 360. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-51142-3_81
Download citation
DOI: https://doi.org/10.1007/3-540-51142-3_81
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-51142-7
Online ISBN: 978-3-540-46163-0
eBook Packages: Springer Book Archive