Modelo de Recomendación Personalizada en Cursos Virtuales basado en Computación Ubicua y Agentes Inteligentes.

The aim of this paper is to propose a customized recommendation model of educational resources for virtual courses that incorporates the benefits of ubiquitous computing and intelligent agents. This model is intended to provide relevant and personalized information to students about their virtual courses planning, online assessment, search and retrieval of learning objects. The methodology used for the construction of the system is MAS-CommonKADS which offers useful models for the phases of conceptualization, analysis and design expressed through artifacts provided by the Agent Unified Modeling Language (AUML). The prototype developed exhibits intelligent proactive and deliberative agents that allow the search and recommendation of information adapted to the student's profile. To validate the system a testing phase on a learning scenario was performed which demonstrate the effectiveness of using this kind of technology within ubiquitous virtual learning environments. [ABSTRACT FROM AUTHOR]