• DocumentCode
    1796736
  • Title

    To what extend can we predict students´ performance? A case study in colleges in South Africa

  • Author

    Poh, Norman ; Smythe, Ian

  • Author_Institution
    Dept. of Comput., Univ. of Surrey, Guildford, UK
  • fYear
    2014
  • fDate
    9-12 Dec. 2014
  • Firstpage
    416
  • Lastpage
    421
  • Abstract
    Student performance depends upon factors other than intrinsic ability, such as environment, socio-economic status, personality and familial-context. Capturing these patterns of influence may enable an educator to ameliorate some of these factors, or for governments to adjust social policy accordingly. In order to understand these factors, we have undertaken the exercise of predicting student performance, using a cohort of approximately 8,000 South African college students. They all took a number of tests in English and Maths. We show that it is possible to predict English comprehension test results from (1) other test results; (2) from covariates about self-efficacy, social economic status, and specific learning difficulties there are 100 survey questions altogether; (3) from other test results + covariates (combination of (1) and (2)); and from (4) a more advanced model similar to (3) except that the covariates are subject to dimensionality reduction (via PCA). Models 1-4 can predict student performance up to a standard error of 13-15%. In comparison, a random guess would have a standard error of 17%. In short, it is possible to conditionally predict student performance based on self-efficacy, socio-economic background, learning difficulties, and related academic test results.
  • Keywords
    computer aided instruction; educational institutions; principal component analysis; socio-economic effects; English comprehension test result; PCA; South African college student; academic test result; colleges; dimensionality reduction; governments; learning difficulty; social policy; socio-economic background; standard error; student performance; Computational modeling; Context; Educational institutions; Field effect transistors; Predictive models; Standards;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Data Mining (CIDM), 2014 IEEE Symposium on
  • Conference_Location
    Orlando, FL
  • Type

    conf

  • DOI
    10.1109/CIDM.2014.7008698
  • Filename
    7008698