ISABEL: A Multi-Agent e-Learning system That Supports Multiple Devices

This paper presents an agent-based e-Learning platform which allows the interaction between users and e-Learning Web sites, providing students with useful suggestions about the available educational resources. In traditional agentbased e-Learning systems, each student is supported by a student agent that interacts with a site agent associated to an e-Learning site. However, in the case of large agent communities, the tasks of both the student and the site agent can result significantly heavy, even more if the student agents run on devices with limited resources. To face this issue, in this paper we propose a new multi-agent learning system, called ISABEL. When a student visits an e-Learning site using a given device, a teacher agent associated with the site collaborates with some tutor agents associated with the student, in order to provide him with useful recommendations. Some experimental results show significant advantages obtained by ISABEL in terms of recommendation efficiency and effectiveness.