Identification of felder-silverman learning styles with a supervised neural network

  • Zatarain-Cabada R
  • Barrón-Estrada M
  • Angulo V
 et al. 
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Abstract

In this paper, we present an intelligent tool implemented as a learning
social network. An author can create, display, and share lessons,
intelligent tutoring systems and other components among communities
of learners in web-based and mobile environments. The tutoring systems
are tailored to the student's learning style according to the model
of Felder-Silverman. The identification of the student's learning
style is performed using self-organizing maps. The main contribution
of this paper is the implementation of a learning social network
to create, view and manage adaptive and intelligent tutoring systems
using a new method for automatic identification of the student's
learning style. We present the architecture of the social network,
the method for identifying learning styles, and some experiments
made to the social network.

Author-supplied keywords

  • Intelligent Tutoring Systems
  • Unsupervised neural networks
  • e-Learning 2.0

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