IDEAS home Printed from https://ideas.repec.org/a/ags/ipeceg/386295.html

Using data mining to obtain seemingly hidden information about students

Author

Listed:
  • Paula,Rafael Vieira de
  • Pazolini, Kelly

Abstract

The process of obtaining information from large databases, known as "Knowledge Discovery in Databases" (KDD), has been used by large companies to suggest books, films, or music, respectively, according to user preferences. This was achieved through techniques that analyze patterns to facilitate decision-making. This group of techniques is called data mining. The objective of this study was to use data mining techniques to discover seemingly hidden patterns in a questionnaire regarding student satisfaction in twelve areas of life. The dataset used for this study was collected through a questionnaire answered by a group of students from Master in Business Administration (MBA) courses at an educational institution located in the city of Piracicaba, in the state of São Paulo. This dataset was compiled and analyzed using the "Waikato Environment for Knowledge Analysis" (WEKA) software, which, through data mining techniques, was able to establish trends in student satisfaction in twelve pre-established areas of life. The tool demonstrated the efficiency of data mining techniques in discovering patterns for the proposed scenario.

Suggested Citation

  • Paula,Rafael Vieira de & Pazolini, Kelly, 2018. "Using data mining to obtain seemingly hidden information about students," Revista IPecege, University of Sao Paulo, vol. 4(3).
    • Handle: RePEc:ags:ipeceg:386295
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/386295/files/admin%2C%2B3.%2B200.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    ;

    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:ags:ipeceg:386295. 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: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/dauspbr.html .

    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.