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Teaching binary logistic regression modeling in an introductory business analytics course
Author(s) -
Hoang VietNgu,
Watson Justin
Publication year - 2022
Publication title -
decision sciences journal of innovative education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 19
eISSN - 1540-4609
pISSN - 1540-4595
DOI - 10.1111/dsji.12274
Subject(s) - logistic regression , analytics , computer science , learning analytics , business analytics , regression analysis , business statistics , course (navigation) , mathematics education , data science , machine learning , statistics , psychology , marketing , mathematics , business model , engineering , business analysis , business , aerospace engineering
There is an increasing demand to introduce Introductory Business Analytics (IBA) courses into undergraduate business education. Many real‐world business contexts require predictive analytics to understand the determinants of a dichotomous outcome; hence, IBA courses should include binary logistic regression analysis. This article provides our reflective discussions on the design of learning activities and assessments to assist business students in learning binary logistic regression in an IBA course. Data on student engagement and learning outcomes are used to shed light on the impacts of teaching logistic regression on student learning and experience. Notably, students opt to focus their assessment work more on logistic regression than on multiple regression analysis, showing the potential attraction of students toward binary logistic regression analysis. We also observed several challenges, mainly related to the use of Excel, that require special attention from instructors.