• DocumentCode
    2151839
  • Title

    Predicting Faculty Performance Using Regression Model in Data Mining

  • Author

    Bermudez, Raymond S. ; Gerardo, Bobby D. ; Manalang, Joseph O. ; Tanguilig, Bartolome T., III

  • Author_Institution
    Coll. of Comput. Studies, Manuel S. Enverga Univ. Found., Lucena City, Philippines
  • fYear
    2011
  • fDate
    10-12 Aug. 2011
  • Firstpage
    68
  • Lastpage
    72
  • Abstract
    This paper investigates the different attributes used in evaluating faculty performance to come up with a regression model that predicts faculty performance. The main objective of this paper is to develop a model for predicting faculty performance and design a framework of data mining implementing ETL. The outcome of this research could be used as basis in improving the instruction in an academic institution.
  • Keywords
    data mining; regression analysis; ETL; academic institution; data mining; faculty performance prediction; regression model; Algorithm design and analysis; Clustering algorithms; Data mining; Partitioning algorithms; Prediction algorithms; Predictive models; Regression analysis; Data mining; clustering; database; predictive algorithm; regression analysis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering Research, Management and Applications (SERA), 2011 9th International Conference on
  • Conference_Location
    Baltimore, MD
  • Print_ISBN
    978-1-4577-1028-5
  • Type

    conf

  • DOI
    10.1109/SERA.2011.29
  • Filename
    6065621
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2151839