• DocumentCode
    2086557
  • Title

    Competency-Based Intelligent Curriculum Sequencing Using Particle Swarms

  • Author

    De-Marcos, Luis ; Barchino, Roberto ; Martinez, J.-J. ; Gutierrez, Jose-Antonio

  • Author_Institution
    Comput. Sci. Dept., Univ. of Alcala, Alcala de Henares
  • fYear
    2008
  • fDate
    1-5 July 2008
  • Firstpage
    295
  • Lastpage
    297
  • Abstract
    As a part of many e-learning initiatives, a set of learning units must be arranged in a particular order to meet the learnerspsila requirements. This process is known as sequencing and it is typically performed by instructors, who create wide-public ordered series rather than learner personalized sequences. This paper proposes an innovative intelligent technique for learning object automated sequencing using particle swarms. E-learning standards are promoted in order to ensure interoperability. Competencies are used to define relations among learning objects within a sequence, so that the sequencing problem turns into a permutation problem and a particle swarm optimization algorithm can be applied to solve it. Results demonstrate that the new agent succeeds and it shows a good performance in real and tests scenarios.
  • Keywords
    artificial intelligence; computer aided instruction; multi-agent systems; open systems; particle swarm optimisation; competency-based intelligent curriculum sequencing; e-learning standards; intelligent agent; object automated sequencing; particle swarm optimization algorithm; permutation problem; Assembly; Competitive intelligence; Computer science; Courseware; Electronic learning; Intelligent agent; Multidimensional systems; Particle swarm optimization; Psychology; Testing; E-learning; competency; learning object; particle swarm optimization (PSO); sequencing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Learning Technologies, 2008. ICALT '08. Eighth IEEE International Conference on
  • Conference_Location
    Santander, Cantabria
  • Print_ISBN
    978-0-7695-3167-0
  • Type

    conf

  • DOI
    10.1109/ICALT.2008.295
  • Filename
    4561690