2021 EDSIG Proceedings: Abstract Presentation

Exploratory Study of Lecture Approaches on Data Analytics Tools at Business Schools

Lizzette Perez Lespier
University of North Carolina Wilmington

Avinash Geda
University of North Carolina Wilmington

Shubham Singh
Purdue University Fort Wayne

Nazli Turken
Johns Hopkins University

Abstract
Business analytics is an emerging area in modern business decision-making, gaining interest and popularity for business intelligence results. Business professionals who posses these quantitative abilities are more adept at handling the demands of the global business environments. Mastery of these skills must begin in the classroom and is an essential component of operations and supply chain management, and business analytics curriculums in business schools. To be able to measure past business performance to guide organizations in visualizing and predicting future business performance and outcomes and therefore, assist with decision-making, an overview of analytics is essential in business schools’ curriculums, with an emphasis on predictive analytics. Regression analysis is the most widely used predictive model used to evaluate the relationship between two or more variables, assisting organizations to understand what their data points represent and use them accordingly with the help of business analytical techniques to do better decision-making. This study explores the difference between both pre-recorded and live lectures to teach regression analysis in a university setting. The objective is to understand if one approach results in higher perceived and actual learning over the other. Additionally, mode of lecturing order will be examined to determine students’ preference of lecture style prior to solving the analytical problem. As a result, this study should provide recommendations to different business schools’ curriculums on how to best foster business analytics teachings to efficiently progress towards desired learning goals.