IDEAS home Printed from https://ideas.repec.org/a/inm/orited/v18y2018i2p89-101.html
   My bibliography  Save this article

Impact of Supplemental Instruction on Business Courses: A Statistical Study

Author

Listed:
  • Sinjini Mitra

    (Information Systems and Decision Sciences (ISDS) Department, California State University, Fullerton, Fullerton, California 92831)

  • Zvi Goldstein

    (Information Systems and Decision Sciences (ISDS) Department, California State University, Fullerton, Fullerton, California 92831)

Abstract

Many students in quantitative business courses are struggling. One technique designed to support such students is Supplemental Instruction (SI), which is most popular in the science, technology, engineering, and mathematics (STEM) disciplines. In this paper, we show the positive impact of SI on student performance in two bottleneck business courses in a large university. Our evaluation results establish that (i) SI has a statistically significant effect on students’ likelihood of passing both courses (after controlling for background variables), (ii) SI is more helpful for students identified as at risk than for those who are not, and (iii) it is important to consistently attend SI sessions for greater success. We also present models to predict consistent student attendance based on background factors with 90% accuracy and conclude with a brief qualitative study about students’ self-perception of SI and the professional development attained by SI leaders.

Suggested Citation

  • Sinjini Mitra & Zvi Goldstein, 2018. "Impact of Supplemental Instruction on Business Courses: A Statistical Study," INFORMS Transactions on Education, INFORMS, vol. 18(2), pages 89-101, January.
    • Handle: RePEc:inm:orited:v:18:y:2018:i:2:p:89-101
    DOI: 10.1287/ited.2017.0178
    as

    Download full text from publisher

    File URL: https://doi.org/10.1287/ited.2017.0178
    Download Restriction: no
    File URL: https://libkey.io/10.1287/ited.2017.0178?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Stacey Vaziri & Baback Vaziri & Luis J. Novoa & Elham Torabi, 2022. "Academic Motivation in Introductory Business Analytics Courses: A Bayesian Approach," INFORMS Transactions on Education, INFORMS, vol. 22(2), pages 121-129, January.
    2. Sinjini Mitra, 2023. "How Are Students Learning in a Business Statistics Course? Evidence from Both Direct and Indirect Assessment," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 95-103, January.

    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:inm:orited:v:18:y:2018:i:2:p:89-101. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.