Building and Assessing Intelligent Tutoring Systems with an e-Learning 2.0 Authoring System

Knowledge Societies also named Social Learning Networks (SLN) allow interaction and collaboration between individuals (instructors and students), who share their connections under a scheme of learning communities around common learning interest. In this paper, we present Zamná, a Knowledge Society implemented as an adaptive learning social network. A community of Instructors and Learners can create, display, share and assess communities, intelligent tutoring systems or adaptive courses in a collaborative environment. The communities and courses are tailored to the student's learning style according to the learning style model of Felder-Silverman. The identification of community’s and student's learning style is performed using self-organizing maps. The main contribution of this paper lies at the integration of Artificial Intelligence with SLN.