• DocumentCode
    2579271
  • Title

    Study on Blended Learning approach for English teaching

  • Author

    Wang, Xiaomei ; Yang, Yajun ; Wen, Xin

  • Author_Institution
    Sch. of Int. Languages & Cultures, Beijing Union Univ., Beijing, China
  • fYear
    2009
  • fDate
    11-14 Oct. 2009
  • Firstpage
    4641
  • Lastpage
    4644
  • Abstract
    In this paper, the authors proposed a novel English teaching model based on blended learning to assist students in successfully mastering the courses. The model is realized as a combination of a face-to-face environment and online learning using an English teaching system. In the system, student dynamic model is established with semantics. The personalized learning resource recommendation is made by support vector machines (SVMs) classifier based on the student dynamic model. The framework of learning resource management using domain ontology is described. A case study on blended learning approach for English teaching is given. The results indicate that blended learning provides an effective approach for the English teaching.
  • Keywords
    computer aided instruction; educational courses; learning (artificial intelligence); ontologies (artificial intelligence); support vector machines; teaching; English teaching model; blended learning approach; domain ontology; educational courses; online learning; personalized learning resource recommendation; student dynamic model; support vector machines; Cybernetics; Education; Educational institutions; Educational technology; Electronic learning; Information technology; Internet; Machine learning; Natural languages; Support vector machines; Blended Learning; English teaching; learning resource; personalized recommendation; student model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man and Cybernetics, 2009. SMC 2009. IEEE International Conference on
  • Conference_Location
    San Antonio, TX
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4244-2793-2
  • Electronic_ISBN
    1062-922X
  • Type

    conf

  • DOI
    10.1109/ICSMC.2009.5346756
  • Filename
    5346756