Abstract
In intelligent tutoring systems (ITS) student models are used for diagnosis resulting in didactical choices. Among others, didactical choices concern at a global level decisions about the sequencing of learning materials and at a local level decisions about presenting additional material and remediation. In ITS didactical choices are often implicit. In some cases choices are derived from models of human tutoring behavior. As far as ITS are based on student (and expert) models, which are plausible from the cognitive psychological point of view, implicit didactical choices or human tutor models can be avoided. When a student model is constructed according to the principles of ACT*, predictions of the effects of didactical interventions on learning outcomes can be made. It can be shown that learning outcomes as speed of processing and probability of correct response are mediated by the storage-and-strengthening parameters of the control structure. Storage and strengthemng in turn are induced by specific types of instruction, such as outcome feedback, working through examples, and providing the correct answer. A provided theoretical analysis explains why the buggy rule approach is, as such, of limited benefit. The results of this analysis are further illustrated by empirical research. The comparison of our predictions about the effects of feedback with the results obtained in empirical research in the domain of concept learning shows that optimal provisions for feedback (direct or postponed) depend on the type of learning: deterministic or probabilistic. This factor should be taken into account when designing didactical modules. Keywords: ITS, intelligent tutoring systems, student models, didactical interventions.
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