Abstract
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.
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Barrón-Estrada, M.L., Zatarain-Cabada, R., Zatarain-Cabada, R., Barbosa-León, H., Reyes-García, C.A. (2010). Building and Assessing Intelligent Tutoring Systems with an e-Learning 2.0 Authoring System. In: Kuri-Morales, A., Simari, G.R. (eds) Advances in Artificial Intelligence – IBERAMIA 2010. IBERAMIA 2010. Lecture Notes in Computer Science(), vol 6433. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16952-6_1
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DOI: https://doi.org/10.1007/978-3-642-16952-6_1
Publisher Name: Springer, Berlin, Heidelberg
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