A Business-Analytic Approach to Identify Critical Factors in Quantitative Disciplines
Most business students in universities across the United States find the quantitatively oriented courses challenging to comprehend the course material to a degree necessary to develop capability and confidence level to solve business problems. A determination of critical factors that influence performance in such courses is critical to designing class instructions. Instructors teaching these classes agonize over the fact that these courses are amongst the most difficult to teach as they encompass relatively harder concepts transformed into analytical skill sets with real applications to business operations that students struggle to grasp. This study employs a machine learning-based approach to determine critical success factors by analyzing the dataset of a focus course and provides some guidelines to educators for improving their teaching effectiveness. Information fusion-based sensitivity analyses on the data mining models provide an unbiased weighting scheme for the rank order of the variables that help predict the students' comprehension level.