Efficient Personalization of E-Learning Activities Using a Multi-Device Decentralized Recommender System

Personalization is becoming a key issue in designing effective e-learning systems and, in this context, a promising solution is represented by software agents. Usually, these systems provide the student with a student agent that interacts with a site agent associated with each e-learning site. However, in presence of a large number of students and of e-learning sites, the tasks of the agents are often onerous, even more if the student agents run on devices with limited resources. To face this problem, we propose a new multiagent learning system, called ISABEL. Our system provides each student, that are using a specific device, with a device agent able to autonomously monitor the student’s behavior when accessing e-learning Web sites. Each site is associated, in its turn, with a teacher agent. When a student visits an e-learning site, the teacher agent collaborates with some tutor agents associated with the student, to provide him with useful recommendations.We present both theoretical and experimental results to show that this distributed approach introduces significant advantages in quality and efficiency of the recommendation activity with respect to the performances of other past recommenders.