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The Foundations and Architecture of Autotutor

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1452)

Abstract

The Tutoring Research Group at the University of Memphis is developing an intelligent tutoring system which takes advantages of recent technological advances in the areas of semantic processing of natural language, world knowledge representation, multimedia interfaces, and fuzzy descriptions. The tutoring interaction is based on in-depth studies of human tutors, both skilled and unskilled. Latent semantic analysis will be used to semantically process and provide a representation for the student’s contributions. Fuzzy production rules select appropriate topics and tutor dialogue moves from a rich curriculum script. The production rules will implement a variety of different tutoring styles, from a basic untrained tutor to one which uses sophisticated pedagogical strategies. The tutor will be evaluated on the naturalness of its interaction, with Turing-style tests, by comparing different tutoring styles, and by judging learning outcomes.

Keywords

Production Rule Latent Semantic Analysis Intelligent Tutoring System World Knowledge Human Tutor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  1. 1.Department of PsychologyThe University of MemphisMemphis
  2. 2.Department of Mathematical SciencesThe University of MemphisMemphis
  3. 3.College of EducationThe University of MemphisMemphis

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