Analyzing knowledge decay and gender differences on end of program assessment measures: Case of a Mid-South University in the USA

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Assessment of student learning outcomes is a nearly universal process for all academic programs, especially those seeking discipline specific accreditation. Business programs typically devote considerable resources toward measuring and assessing student learning outcomes with the goal of ‘closing the loop’ by making curricula changes to improve student learning outcomes. Most of these assessment efforts utilize relatively small samples of course-level observations and employ little to no statistical framework to derive conclusions. This study seeks to provide programs with a framework that allows institutions to examine student performance on an end of program assessment test for business, the ETS Major Field Test in Business (MFT-B). We examine the relationship between student demographics, course timing, and knowledge decay in the 9 subject areas on the MFT-B by employing a system of simultaneous equations estimated within a seemingly unrelated regression (SUR) framework with data partitioning by gender. The data was collected at a university in the mid-south in the United States of America. Our results indicate that gender and course timing can affect student performance on the MFT-B. These findings indicate that the SUR framework and data partitioning are valuable tools in the assessment and analysis of student learning outcomes.