By Etienne Wenger
Contributor note: ahead by way of John Seely Brown & James Greeno
Publish 12 months note: First released in 1987
Artificial Intelligence and Tutoring Systems, the 1st complete reference textual content during this dynamic sector, surveys study because the early Nineteen Seventies and assesses the cutting-edge. Adopting the point of view of the verbal exchange of data, the writer addresses sensible concerns excited by designing educational platforms in addition to theoretical questions raised through investigating computational tools of information verbal exchange.
Weaving jointly the ambitions, contributions, and engaging demanding situations of clever tutoring procedure improvement, this well timed ebook turns out to be useful as a textual content in classes on clever tutoring structures or computer-aided guideline, an creation for beginners to the sphere, or as a reference for researchers and practitioners.
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Additional info for Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge
While theoretically limited in scope, this enumerative approach has the practical advantage that the knowledge about the likelihood of errors it incorporates can be derived empirically or obtained from expert teachers. By generating diagnostic 18 Basic issues expectations, this knowledge focuses the search for a student model on previously observed behavior patterns. For current applications, the laborious development of such "catalogs" of errors for various domains is an important activity of designers.
Other pedagogically oriented pieces of domain knowledge include rationales for explanations in terms of goals and causes, as well as conceptual or taxonomic relations between pieces of knowledge that facilitate the use of analogies and abstractions. With respect to learning, the nature of the expert model itself is crucial. Even when the expert is a distinct module capable of repro ducing expertise, its internal structure may be more or less open to inspection, and more or less able to support explanations of its actions and conclusions.
This simulation allows the verification of precise hypotheses as to the nature of a student's misconceptions. The diagnostic process: accounting for data The process of forming and updating the student model by analyzing data made available to the system is often called diagnosis. Numerical values grading performance can mostly be updated with some simple statistical computations. In contrast, detailed diagnosis usually requires search, whether the system is trying to reconstruct a goal structure to interpret the student's behavior or attempting to model his knowledge.