Titlebook: Intelligent Learning Environments and Knowledge Acquisition in Physics; Andrée Tiberghien,Heinz Mandl Conference proceedings 1992 Springer

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Michel Caillotch verknüpft sind. Kapitel?8 stellt?– dem Rechnung tragend?– daher die alles entscheidende Frage: Cui bono? Wem zum Vorteil?.Jede neue Entwicklung wird immer zuerst skeptisch be?ugt. Zeigt sie erste Erfolge für die Forscher und Entwickler und darüber hinaus auch für Anwendungen, die Menschen ihre Ar

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0258-1248 glia Practical Work Aid: Knowledge Representation in a Model Based AI System 21 J. Courtois Simultaneous Processing of Different Problem Aspects in Expert Probl978-3-642-84786-8978-3-642-84784-4Series ISSN 0258-1248

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Emilio Balzano,Paolo Guidoni,Maria Moretti,Elena Sassi,Gabriella Sgueglia

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Practical Work Aid: Knowledge Representation in a Model Based AI Systemeps of the cognitive process followed by the experts. Most of the knowledge is represented by using models and descriptives. The advantages of this approach are both technical and pedagogical, by allowing easier acquisition of knowledge, reusability of the models and construction of relevant explana

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Cognitive Theories as a Basis for Student ModellingA 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 phys

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Conference proceedings 1992 total of 31 researchers from Europe (France, Germany, Greece, Italy, Portugal, and the U. K. ), the U. S. A. , and Japan worked together. This proceedings volume contains most of the contributions to the workshop. The papers show clearly the main directions of research in intelligent learning envir

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Introductory University Courses and Open Environment Approaches: the Computer as a Multi-Role Mediations; . approximate approach to . situations. We suggest that well planned computer use and substantial requalification of the teaching effort can extensively and qualitatively improve the average level of understanding.

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Eliciting Hypothesis-Driven Learning in a Computer-Based Discovery Environmentway that provides both structure to students’ task and a window on their hypothesis generation and testing behavior. The system adapts in some sense to students’ knowledge by permitting them to state predictions in several ways, ranging from purely qualitative/graphical to quantitative.