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.
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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
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DOI: https://doi.org/10.1007/3-540-51142-3_81
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