By Etienne Wenger
Contributor note: ahead through John Seely Brown & James Greeno
Publish yr note: First released in 1987
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Artificial Intelligence and Tutoring Systems, the 1st complete reference textual content during this dynamic quarter, surveys learn because the early Seventies and assesses the state-of-the-art. Adopting the point of view of the verbal exchange of information, the writer addresses useful concerns desirous about designing educational platforms in addition to theoretical questions raised by way of investigating computational tools of data verbal exchange.
Weaving jointly the targets, contributions, and engaging demanding situations of clever tutoring procedure improvement, this well timed e-book turns out to be useful as a textual content in classes on clever tutoring platforms or computer-aided guide, an advent for rookies to the sector, or as a reference for researchers and practitioners.
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Additional resources for Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge
Example text
Thus, although ITS research concentrates on situations where there are long-term intentions to cause modifications to the state of knowledge of interlocutors, there remains a large overlap between the two fields: for instance, the articulation of tacit conventions such as conversational postulates, or the use of models of participants. Anthropology is another related field, inasmuch as the study of cognitive processes central to designing explicit models of knowledge communication cannot isolate itself from the cultural fabric and situational dimension in which interactions are to take place and in which knowledge evolves (Suchman, 1987; Lave, in press).
2 Student model: the recipient of communication No intelligent communication can take place without a certain under standing of the recipient. Thus, along with the idea of explicitly repre senting the knowledge to be conveyed came the idea of doing the same with the student, in the form of a student model Ideally, this model should include all the aspects of the student's behavior and knowledge that have repercussions for his performance and learning. However, the task of constructing such a student model is obviously not a simple one for computer-based systems.
Conversely, we will see that some psychologists have incorporated the purposes and methodologies of ITS into their research and have found instructional systems to be good research tools in the development of cognitive theories. Obviously, education and educational psychology are very closely related research areas, in their attempts to capture the constituents of pedagogical expertise, to gather a corpus of useful empirical techniques, and to produce useful instructional artifacts. In return, the episte mological attitude adopted for computational approaches provides new opportunities to study curricula and teaching strategies.