• DocumentCode
    2088787
  • Title

    LRMDCR: A Learner´s Role-Based Multi Dimensional Collaborative Recommendation for Group Learning Support

  • Author

    Wan, Xin ; Ninomiya, Toshie ; Okamoto, Toshio

  • Author_Institution
    Grad. Sch. of Inf. Syst., Univ. of Electro-Commun., Chofu
  • fYear
    2008
  • fDate
    1-5 July 2008
  • Firstpage
    603
  • Lastpage
    605
  • Abstract
    In order to improve the ldquoeducational provisionrdquo to implement the e-learning recommender system, we propose a new recommendation approach which has been proven to be more suitable to realize personalized recommendation based on not only learning histories but also learning activities and learning processes which is defined as LRMDCR (a learnerpsilas role-based multidimensional collaborative recommendation). In the approach, firstly we use the Markov chain model to divide the group learners into advanced learners and beginner learners by using the learnerspsila learning activities and learning processes. Secondly we use the multidimensional collaborative filtering to decide the recommendation learning objects to every learner of the group.
  • Keywords
    Markov processes; computer aided instruction; information filters; LRMDCR; Markov chain model; e-learning recommender system; group learning support; learner role-based multi dimensional collaborative recommendation; multidimensional collaborative filtering; Collaborative work; Educational technology; Electronic learning; Filtering; History; Information systems; International collaboration; Multidimensional systems; Recommender systems; Wide area networks; Markov Chain Model; multidimensional collaborative filtering; role-based;
  • 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.2008.63
  • Filename
    4561780