IDEAS home Printed from https://ideas.repec.org/a/igg/jismd0/v12y2021i1p94-110.html
   My bibliography  Save this article

Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques

Author

Listed:
  • Deepti Aggarwal

    (Jaipur National University, Jaipur, India)

  • Sonu Mittal

    (Jaipur National University, Jaipur, India)

  • Vikram Bali

    (JSS Academy of Technical Education, Noida, India)

Abstract

The educational institutes are focusing on improving the performance of students by using several data mining techniques. Since there is an increase in the number of drop out students every year, if we are able to predict whether a student will complete the course or not, it is possible to take some preventive actions beforehand. The primary data set used for modelling has been taken from a reputed technical institute of Uttar Pradesh which consists of data of 6,807 students containing 20 academic and non-academic attributes. The most relevant attributes are extracted using CorrelationAttributeEval (in WEKA) technique using Ranker search method which ranks the attributes as per their evaluation. Synthetic minority oversampling technique (SMOTE) filter is applied to deal with the skewed data set. The models are built from eight classifiers that are analysed for predicting the most appropriate model to classify whether a student will complete the course or withdraw his/her admission.

Suggested Citation

  • Deepti Aggarwal & Sonu Mittal & Vikram Bali, 2021. "Identifying Non-Performing Students in Higher Educational Institutions Using Data Mining Techniques," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 12(1), pages 94-110, January.
    • Handle: RePEc:igg:jismd0:v:12:y:2021:i:1:p:94-110
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISMD.2021010105
    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:jismd0:v:12:y:2021:i:1:p:94-110. 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.