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Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning
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Author(s): Shweta (Banasthali University, India), Praveen Dhyani (Banasthali University, India)and O. P. Rishi (University of Kota, India)
Copyright: 2015
Pages: 26
Source title:
Artificial Intelligence Applications in Distance Education
Source Author(s)/Editor(s): Utku Kose (Usak University, Turkey)and Durmus Koc (Usak University, Turkey)
DOI: 10.4018/978-1-4666-6276-6.ch013
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Abstract
Intelligent Tutoring Systems have proven their worth in multiple ways and in multiple domains in education. In this chapter, the proposed Agent-Based Distributed ITS using CBR for enhancing the intelligent learning environment is introduced. The general architecture of the ABDITS is formed by the three components that generally characterize an ITS: the Student Model, the Domain Model, and the Pedagogical Model. In addition, a Tutor Model has been added to the ITS, which provides the functionality that the teacher of the system needs. Pedagogical strategies are stored in cases, each dictating, given a specific situation, which tutoring action to make next. Reinforcement learning is used to improve various aspects of the CBR module: cases are learned and retrieval and adaptation are improved, thus modifying the pedagogical strategies based on empirical feedback on each tutoring session. The student modeling is a core component in the development of proposed ITS. In this chapter, the authors describe how a Multi-Agent Intelligent system can provide effective learning using Case-Based Student Modeling.
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