Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/73956
Título: Bayesian model for optimization adaptive e-learning process
Autores/as: Tapia, Francisco
Lopez, C. A.
Galan, M. J.
Rubio Royo, Enrique 
Clasificación UNESCO: 33 Ciencias tecnológicas
Palabras clave: Bayesian Networks
E-Learning
Learning Metrics
Fecha de publicación: 2008
Publicación seriada: International Journal of Emerging Technologies in Learning 
Resumen: In this paper, a Bayesian-Network-based model is proposed to optimize the Global Adaptive e-Learning Process (GAeLP). This model determines the type of personalization required for a learner according to his or her real needs, in which we have considered both objects and objectives of personalization. Furthermore, cause-andeffect relations among these objects and objectives with the learning phases, the learner, and the Intelligent Tutorial System (ITS) are accomplished. These cause-and-effect relations were coded into a Bayesian Network (BN), such that it involves the entire GAeLP. Four fundamental phases that have a direct effect in the learner's learning process are considered: Learner's previous knowledge Phase, Learner's Progress Knowledge Phase, Learner's /Teacher's Aims and Goals Phase, and Navigation Preferences and Experiences Phase. The efficacy of the Bayesian networks is proven through the first phase, in which learners of different knowledge area were select. The main results in this work are: causal relations among objects and objectives of personalization, knowledge phases, learner and electronic system. Personalization profiles set and their probabilities in the first phase were obtained to diagnose the type of personalization of the learner.
URI: http://hdl.handle.net/10553/73956
ISSN: 1868-8799
Fuente: International Journal of Emerging Technologies in Learning [ISSN 1868-8799], v. 3 (2), p. 38-52, (Diciembre 2008)
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