IDEAS home Printed from https://ideas.repec.org/a/igg/jtem00/v4y2014i2p43-53.html
   My bibliography  Save this article

Predicting Student Retention by Linear Programming Discriminant Analysis

Author

Listed:
  • Jaan Ubi

    (Perdue School of Business, Salisbury University, Salisbury, MD, USA)

  • Evald Ubi

    (Tallinn University of Technology, Tallinn, Estonia)

  • Innar Liiv

    (Tallinn University of Technology, Tallinn, Estonia)

  • Kristina Murtazin

    (Tallinn University of Technology, Tallinn, Estonia)

Abstract

The goal of the paper is to predict student retention with an ensemble method by combining linear programming (LP) discriminant analysis approaches together with bootstrapping and feature salience detection. In order to perform discriminant analysis, we linearize a fractional programming method by using Charnes-Cooper transformation (CCT) and apply linear programming, while comparing with an approach that uses deviation variables (DV) to tackle a similar multiple criteria optimization problem. We train a discriminatory hyperplane family and make the decision based on the average of the histograms created, thereby reducing variability of predictions. Feature salience detection is performed by using the peeling method, which makes the selection based on the proportion of variance explained in the correlation matrix. While the CCT method is superior in detecting true-positives, DV method excels in finding true-negatives. The authors obtain optimal results by selecting either all 14 (CCT) or the 8 (DV) most important student study related and demographic dimensions. They also create an ensemble. A quantitative course along with the age at accession are deemed to be the most important, whereas the two courses resulting in less than 2% of failures are amongst the least important, according to peeling. A five-fold Kolmogorov-Smirnov test is undertaken, in order to help university staff in devising intervention measures.

Suggested Citation

  • Jaan Ubi & Evald Ubi & Innar Liiv & Kristina Murtazin, 2014. "Predicting Student Retention by Linear Programming Discriminant Analysis," International Journal of Technology and Educational Marketing (IJTEM), IGI Global, vol. 4(2), pages 43-53, July.
  • Handle: RePEc:igg:jtem00:v:4:y:2014:i:2:p:43-53
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/ijtem.2014070104
    Download Restriction: no

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jtem00:v:4:y:2014:i:2:p:43-53. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Journal Editor). General contact details of provider: https://www.igi-global.com .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.