• DocumentCode
    2633666
  • Title

    Research on Early-Warning Model of Students´ Academic Records Based on Association Rules

  • Author

    Zhu, Li ; Li, Yanli ; Li, Xiang

  • Author_Institution
    Sch. of Comput., China Univ. of Geosci., Wuhan, China
  • Volume
    4
  • fYear
    2009
  • fDate
    March 31 2009-April 2 2009
  • Firstpage
    121
  • Lastpage
    125
  • Abstract
    Association rules is an important research direction of data mining. Its study is mostly concentrated on improving algorithm efficiency presently, but neglects userspsila understanding and participating in excavating course. Studentspsila historical academic records stored in university´s educational administration systems was taken as data source, the paper established interactive visible mining model based on classical association rules, and introduced objective interest degree and subjective interest degree. Experiment results show that model built was feasible and meaningful; it could help us improve teaching management and personnel trainingspsila quality.
  • Keywords
    data mining; educational administrative data processing; teaching; association rules; data mining; early-warning model; interactive visible mining model; personnel training quality; student academic record; teaching management; university educational administration system; Association rules; Computer science; Data engineering; Data mining; Education; Geology; Management training; Relational databases; Testing; Training data; Apriori algorithm; Association rules; Early-warning of students´ academic records; Interest measure;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Information Engineering, 2009 WRI World Congress on
  • Conference_Location
    Los Angeles, CA
  • Print_ISBN
    978-0-7695-3507-4
  • Type

    conf

  • DOI
    10.1109/CSIE.2009.282
  • Filename
    5170973