IDEAS home Printed from https://ideas.repec.org/a/igg/jkbo00/v6y2016i2p38-54.html
   My bibliography  Save this article

Comparing Data Mining Models in Academic Analytics

Author

Listed:
  • Dheeraj Raju

    (School of Nursing, University of Alabama at Birmingham, Birmingham, AL, USA)

  • Randall Schumacker

    (College of Education, The University of Alabama, Tuscaloosa, AL, USA)

Abstract

The goal of this research study was to compare data mining techniques in predicting student graduation. The data included demographics, high school, ACT profile, and college indicators from 1995-2005 for first-time, full-time freshman students with a six year graduation timeline for a flagship university in the south east United States. The results indicated no difference in misclassification rates between logistic regression, decision tree, neural network, and random forest models. The results from the study suggest that institutional researchers should build and compare different data mining models and choose the best one based on its advantages. The results can be used to predict students at risk and help these students graduate.

Suggested Citation

  • Dheeraj Raju & Randall Schumacker, 2016. "Comparing Data Mining Models in Academic Analytics," International Journal of Knowledge-Based Organizations (IJKBO), IGI Global, vol. 6(2), pages 38-54, April.
    • Handle: RePEc:igg:jkbo00:v:6:y:2016:i:2:p:38-54
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJKBO.2016040103
    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:jkbo00:v:6:y:2016:i:2:p:38-54. See general information about how to correct material in RePEc.

    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 bibliographic 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.

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

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.