• DocumentCode
    1571388
  • Title

    Optimal grouping by using Genetic Algorithm and Support Vector Machines

  • Author

    Lin, Kuan-Cheng ; Shiau, Mei-Lian ; Lin, Shu-Ying ; Tai, Jui

  • Author_Institution
    Dept. of Manage. Inf. Syst., Nat. Chung Hsing Univ., Taichung, Taiwan
  • fYear
    2009
  • Firstpage
    777
  • Lastpage
    782
  • Abstract
    Nowadays with bloom of Internet technology, students can do their learning by digital learning platform even they are not in class. To diversify teaching and to improve effects in learning, many researchers strongly advocate cooperative learning and come out many essays in the field. When it comes to grouping, making group members help others, learning each other, and making students progressed in cooperative learning are prospective subjects. This investigation features collecting on-line learning processes and personal thinking styles of learners to select main characteristics influenced grouping by genetic algorithm. We also predict optimal grouping way which decided by support vector machine, one of machine learning. Finding best grouping model from varied characteristics helps teacher group easily and further the whole learning results in cooperative learning.
  • Keywords
    Internet; computer aided instruction; genetic algorithms; learning (artificial intelligence); support vector machines; Internet technology; cooperative learning; digital learning platform; genetic algorithm; machine learning; on-line learning processes; support vector machines; teaching; Artificial intelligence; Biological cells; Education; Electronic learning; Genetic algorithms; Information systems; Internet; Machine learning; Support vector machine classification; Support vector machines; Cooperative Learning; Genetic Algorithm; Support Vector Machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pervasive Computing (JCPC), 2009 Joint Conferences on
  • Conference_Location
    Tamsui, Taipei
  • Print_ISBN
    978-1-4244-5227-9
  • Electronic_ISBN
    978-1-4244-5228-6
  • Type

    conf

  • DOI
    10.1109/JCPC.2009.5420079
  • Filename
    5420079