MASHA-EL: A multi-agent system for supporting adaptive e-learning

Agent-based e-learning systems allow the interaction between students and e-learning Web sites, providing students with useful suggestions about the available educational resources. In these systems, generally each student is monitored by a student agent, while each e-learning site is associated with a site agent. However, these systems are not able to support students which exploit different devices as PC, palmtop, cellular, etc. multi-agent system handling adaptivity (MASHA) is a recent multi-agent system which appears as a promising candidate to overcome such a limitation. However, in the case of large agent communities, the tasks of both the student and the site agent in MASHA can result significantly heavy. To face this issue, we propose in this paper an extension of the MASHA system, called MASHA-EL, appositely conceived for supporting e-learning. In this system, a student that exploits a given device is provided with a device agent, and each e-learning Web site is associated, in its turn, with a teacher agent. When a student visits the e-Learning site of the teacher agent using a given device, the teacher agent provide him with useful recommendations also considering the exploited device. We present some experimental results that compares the performances of MASHA-EL with other well- known agent-based recommenders, and that show significant advantages obtained by MASHA-EL in terms of recommendation effectiveness and reduction of time costs.