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Case-Based Reasoning to Improve Adaptability of Intelligent Tutoring Systems

Full text:  
Authors: Peter Funk, Owen Conlan (external)
Source: Workshop on Case-Based Reasoning for Education and Training, CBRET'2002, p 15-23, Robert Gordon University, Aberdeen, Scotland
 

Abstract

Educational Adaptive Hypermedia Systems and Intelligent Tutoring Systems (ITS) are capable of producing personalized learning courses that are tailored to various learning preferences and characteristics of the learner. In the past ITS traditionally have embedded experts’ knowledge in the structure of its content and applied appropriate design models. However, such systems have continually been criticized for believing that this is sufficient for effective learn-ing to occur [Stauffer 96]. For a tutor who develops such a system there may be many permutations of narrative, concepts and content that may be combined to produce the learner courses. However, the more levels of personalization the system can provide the greater likelihood exists that the system may produce an unexpected or undesired effect. As a tutor it can be difficult to monitor the suit-ability of the personalized course offerings on an individual learner basis. This paper provides a high level overview of a technique for predicting/monitoring personalized course suitability and increasing the quality of delivered courses using CBR in combination with other techniques, e.g. filtering techniques.
 

BibTeX entry

@inproceedings{Funk_0420:2002,
   author = {Peter Funk and Owen Conlan},
   title = {Case-Based Reasoning to Improve Adaptability of Intelligent Tutoring Systems},
   booktitle = {Workshop on Case-Based Reasoning for Education and Training, CBRET'2002},
   month = {September},
   year = {2002},
   pages = {15-23},
   publisher = {Robert Gordon University},
   url = {http://www.mrtc.mdh.se/index.php?choice=publications&id=0420},
}

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