• DocumentCode
    2563008
  • Title

    Academic Performance Predictors

  • Author

    Cheng Lei ; Kin Fun Li

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Victoria, Victoria, BC, Canada
  • fYear
    2015
  • fDate
    24-27 March 2015
  • Firstpage
    577
  • Lastpage
    581
  • Abstract
    The ability to predict one´s academic performance is a great asset for both the students and the institution administrators. For the students, they can adjust workload, career direction, etc. If they are aware of their capability. For the administrators and instructors, early warnings would facilitate intervention thus enabling a more successful academic environment. In addition, institutional resources can be utilized in an optimal way thus gaining operation efficiency. This work surveys existing literature in student academic performance prediction. Parameters used for predictions are examined. Useful predictors are identified.
  • Keywords
    educational administrative data processing; educational institutions; academic environment; academic performance predictors; career direction; institution administrators; institutional resources; operation efficiency; student academic performance; Computational modeling; Computers; Conferences; Data mining; Education; Neural networks; Predictive models; academic performance; performance metrics; student classification; success predictors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Advanced Information Networking and Applications Workshops (WAINA), 2015 IEEE 29th International Conference on
  • Conference_Location
    Gwangiu
  • Print_ISBN
    978-1-4799-1774-7
  • Type

    conf

  • DOI
    10.1109/WAINA.2015.114
  • Filename
    7096239