scholarly journals Designing Intelligent Tutoring Systems: A Personalization Strategy using Case-Based Reasoning and Multi-Agent Systems

2013 ◽  
Vol 2 (1) ◽  
pp. 41-54
Author(s):  
Carolina González ◽  
Juan Carlos Burguillo ◽  
Martín Llamas ◽  
Rosalía Laza
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Agents ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Educational Systems ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Tutoring Systems ◽  
Student Profile ◽  
Case Based

Intelligent Tutoring Systems (ITSs) are educational systems that use artificial intelligence techniques for representing the knowledge. ITSs design is often criticized for being a complex and challenging process. In this article, we propose a framework for the ITSs design using Case Based Reasoning (CBR) and Multiagent systems (MAS). The major advantage of using CBR is to allow the intelligent system to propose smart and quick solutions to problems, even in complex domains, avoiding the time necessary to derive those solutions from scratch. The use of intelligent agents and MAS architectures supports the retrieval of similar students models and the adaptation of teaching strategies according to the student profile. We describe deeply how the combination of both technologies helps to simplify the design of new ITSs and personalize the e-learning process for each student

Agent-Based Distributed Intelligent Tutoring System Using Case-Based Reasoning

2018 ◽  
pp. 901-928
Author(s):  
Shweta ◽  
Praveen Dhyani ◽  
O. P. Rishi
Keyword(s):  
Intelligent System ◽  
Intelligent Tutoring ◽  
Intelligent Tutoring System ◽  
Domain Model ◽  
Student Modeling ◽  
Specific Situation ◽  
Pedagogical Strategies ◽  
Tutoring Systems ◽  
Agent Based ◽  
Case Based

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.

scholarly journals ALLEGRO: Teaching/Learning Multi-Agent Environment using Instructional Planning and Cases- Based Reasoning (CBR)

2007 ◽  
Vol 10 (1) ◽  
Author(s):  
Rosa M. Viccari ◽  
Demetrio A. Ovalle ◽  
Jovani A. Jimenez
Keyword(s):  
Intelligent Tutoring Systems ◽  
Instructional Planning ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Computer Supported Collaborative Learning ◽  
Individualized Learning ◽  
Tutoring Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Teaching Learning

This paper presents a description of the environments of individualized learning (Based on the Intelligent Tutoring Systems, ITS), the Computer Supported Collaborative Learning (CSCL), Multi-Agent Systems (MAS) and the artificial intelligence techniques called: Instruc- tional Planning (IP) and Case-Based Reasoning (CBR). Finally ALLEGRO is presented, a MAS environment of support to the teaching/learning process that includes all previous artificial in- telligence elements.

Special Session: Intelligent Tutoring and Help Systems

1987 ◽  
Vol 31 (3) ◽  
pp. 280-280
Author(s):  
Philip J. Smith ◽  
Elliot Soloway ◽  
John Carroll
Keyword(s):  
Human Factors ◽  
Intelligent Tutoring Systems ◽  
Computational Models ◽  
Human Performance ◽  
Intelligent System ◽  
Intelligent Tutoring ◽  
Special Session ◽  
Research Issues ◽  
Tutoring Systems ◽  
Area Of Interest

In recent years, considerable effort has been focused on the development of computational models of expert human performance. One class of expertise that has been studied is that of human tutors. The resultant intelligent tutoring systems are intended to provide the user with the “instructional advantage that a sophisticated human tutor can provide,” (Anderson, Boyle and Reiser, 1985). This line of research is of interest to the human factors community for two reasons: 1. Intelligent tutoring systems offer potential tools for use in training and educational programs, a long-standing area of interest to human factors researchers and practitioners; 2. There are many human factors and human performance issues that should be addressed in the design of such tutoring systems. The speakers in this special session will provide an overview of research issues in the design of intelligent tutoring systems. Relevant conceptual issues and approaches will be highlighted in the context of a variety of application areas. Included will be a discussion of the “use of intelligent system monitors that allow users to integrate the time and effort spent on learning with actual use of a system”, (Carroll and McKendree, 1987).

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