Agent-based e-Learning systems allow the interaction between students and e-Learning Web sites, providing stu- dents with useful suggestions about the available educa- tional 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 sys- tems 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 sys- tem which appears as a promising candidate to overcome such a limitation. However, in the case of large agent com- munities, the tasks of both the student and the site agent in MASHA can result significantly heavy. To face this is- sue, we propose in this paper an extension of the MASHA system, called MASHA-EL, appositely conceived for sup- porting 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 pro- vide 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 signifi- cant advantages obtained by MASHA-EL in terms of rec- ommendation effectiveness and reduction of time costs.