Bringing AI to E-Learning: The Case of a Modular, Highly Adaptive System

Bringing AI to E-Learning: The Case of a Modular, Highly Adaptive System

K. Giotopoulos (University of Patras, Greece), C. Alexakos (University of Patras, Greece), G. Beligiannis (University of Patras, Greece) and A. Stefani (University of Patras, Greece)
Copyright: © 2012 |Pages: 13
DOI: 10.4018/978-1-61350-468-0.ch011

Abstract

This paper presents a newly developed student model agent, which is the basic part of an e-learning environment that incorporates Intelligent Agents and Computational Intelligence Techniques. The e-learning environment consists of three parts, the E-learning platform Front-End, the Student Questioner Reasoning and the Student Model Agent. The basic aim of this contribution is to describe in detail the agent’s architecture and the innovative features it provides to the e-learning environment through its utilization as an autonomous component. Several basic processes and techniques are facilitated through the agent in order to provide intelligence to the e-learning environment.
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2. State Of The Art

Adaptive e-learning systems often employ models of the user. A user model is a representation of the user’s properties and characteristics. Before a user model can be used it has to be constructed. This process requires much effort especially for gathering the required information and for generating a model of the user.

The behavior of an adaptive system varies according to the data from the user model and the user profile. In (Koch, 2000) there is a description of the necessity of applying user models as follows: “Users are different: they have different background, different knowledge about a subject, different preferences, goals and interests. To individualize, personalize or customize actions a user model is needed that allows for selection of individualized responses to the user.”

In the context of e-learning, adaptive systems are more specialized and focus on the adaptation of the learning content and its presentation. According to (Mödritscher et al., 2004), an adaptive system focuses on how the knowledge is learned by the student and pays attention to learning activities, cognitive structures and the context of the learning material.

In Figure 1, the structure of an adaptive system, according to (Brusilovski & Maybury, 2002) with three stages during the process of adaptation is shown. It controls the process of collecting data about the user, the process of building up the user model (user modeling) and the adaptation process.

Figure 1.

The structure of an adaptive system

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