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SI-APRENDE: An Intelligent Learning System Based on SCORM Learning Objects for Training Power Systems Operators

  • Liliana Argotte
  • G. Arroyo-Figueroa
  • Julieta Noguez
Chapter
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Part of the Studies in Computational Intelligence book series (SCI, volume 363)

Abstract

This paper presents the architecture of the Intelligent Learning Systems for training of power systems operators and describes one of its components: the tutor module. Operators can acquire knowledge in different ways or with different paths of learning, also called models of sequence. The tutor model is an adaptive intelligent system that selects the sequence of the learning material presented to each operator. The adaptive sequence is represented as a decision network that selects the best pedagogical action for each specific operator. The decision network represents information about the current state of the tutor, their possible actions, the state resulting from the action of the tutor and the usefulness of the resulting state. The model was evaluated using graduate students with good results. Based on the adaptive model, we developed an Intelligent Learning System called as SI-Aprende. The SI-Aprende system manages, spreads and promotes the knowledge by mean of the search and recovery of SCORM Learning Objects.

Keywords

adaptive learning intelligent environment learning objects SCORM sequencing model 

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References

  1. 1.
    Peachey, D.R., McCalla, G.I.: Using planning techniques in intelligent tutoring systems. Int. Journal Man-Machines Studies 24, 77–98 (1986)CrossRefGoogle Scholar
  2. 2.
    Brusilovsky, P., Vassileva, J.: Course sequencing techniques for large-scale web based education. Int. Journal Cont. Engineering Education and Lifelong Learning 13(1/2), 75–94 (2003)Google Scholar
  3. 3.
    Hernández, Y., Noguez, J., Sucar, E., Arroyo-Figueroa, G.: A probabilistic model of affective behavior for intelligent tutoring systems. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds.) MICAI 2005. LNCS (LNAI), vol. 3789, pp. 1175–1184. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Arroyo-Figueroa, G., Hernández, Y., Reyes, A., Enrique Sucar, L.: Intelligent Environment for training of power systems operators. In: CERMA 2008, pp. 27–31 (2008)Google Scholar
  5. 5.
    Hake, R.: Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. Am. J. Phys. 66(1), 64–74 (1998)CrossRefGoogle Scholar
  6. 6.
    Argotte, L.: Intelligent E- Learning model for adaptive sequence of learning objects. Msc Thesis, Instituto Tecnológico y de Estudios Superiores de Monterrey, Cam-pus Ciudad de México (2010) (in Spanish)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Liliana Argotte
    • 1
  • G. Arroyo-Figueroa
    • 1
  • Julieta Noguez
    • 2
  1. 1.Instituto de Investigaciones ElectricasMorelosMexico
  2. 2.Tecnologico de MonterreyMexicoMexico

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