An Approach for Ushering Logistic Regression Early in Introductory Analytics Courses - Kunene, Niki; Toskin, Katarzyna (Information Systems Education Journal, 2022)
Logistic regression (LoR) is a foundational supervised machine learning algorithm and yet, unlike linear regression, appears rarely taught early on, where analogy and proximity to linear regression would be an advantage. A random sample of 50 syllabi from undergraduate business statistics courses shows only two percent of the courses included LoR. Conceivable reasons for this dearth of LoR content is likely related to topic complexity, time constraints, and varying degrees of tool ease of use and support. We propose that these constraints can be countered by: [1] introducing logistic regression early, [2] informed tool selection prioritizing ease of use with comprehensive output, and [3] using/developing innovative, accessible, and easy to understand concept learning aids. This approach would leverage the proximity to linear regression and probability readily embed distributed practice for student understanding of a foundational technique.
ERIC Number: EJ1363423
Keywords: Business Administration Education; Comparative Analysis; Concept Formation; Course Content; Course Descriptions; Goodness of Fit; Institutional Characteristics; Introductory Courses; Private Colleges; Probability; Regression (Statistics); State Universities; Statistics Education; Teaching Methods; Two Year Colleges; Undergraduate Students; Universities; Usability; dept.bis; peer-reviewed; ⛔ No DOI found

Flow through Regression: A Guide to Interpreting and Communicating Regression Models for Data Analytics Students - Toskin, Katarzyna; Kunene, Niki (Information Systems Education Journal, 2021)
Faculty teaching data analytics at undergraduate level are often faced with the tension created by student under-preparedness, the demands of the course, and time constraints. How do faculty close this gap? In this paper, we propose the use of flow diagramming as an accessible method for interpreting regression analyses, in ways that are time efficient and not alienating to the student. Our study shows that the use of such flow diagrams has a positive effect on student understanding without additional remedial instruction. Time saved can be directed at core learning objectives of the analytics.
ERIC Number: EJ1319909
Keywords: Business Administration Education; College Faculty; College Preparation; Data Analysis; Pretests Posttests; Remedial Instruction; Statistics Education; Teaching Methods; Time Management; Undergraduate Students; Visual Aids; dept.bis; peer-reviewed; ⛔ No DOI found