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.

对所有学术课程，尤其是那些寻求学科特定认证的课程，学生学习成果的评估几乎是一个普遍的过程。商业课程通常会投入大量资源来衡量和评估学生的学习成果，其目标是通过更改课程以改善学生的学习成果来“封闭循环”。这些评估工作大多数都使用相对较小的课程水平观察样本，很少或根本没有统计框架来得出结论。本研究旨在为计划提供一个框架，使机构可以在商业计划评估测试（即ETS商业主要现场测试（MFT-B））结束时检查学生的表现。我们研究了学生人口统计，课程时间，通过在表面上不相关的回归（SUR）框架中估算的联立方程组以及按性别划分的数据，在MFT-B的9个主题领域中知识和知识的衰落。数据是在美国中南部的一所大学收集的。我们的结果表明性别和课程时间会影响学生在MFT-B上的表现。这些发现表明，SUR框架和数据分区是评估和分析学生学习成果的宝贵工具。我们的结果表明性别和课程时间会影响学生在MFT-B上的表现。这些发现表明，SUR框架和数据分区是评估和分析学生学习成果的宝贵工具。我们的结果表明性别和课程时间会影响学生在MFT-B上的表现。这些发现表明，SUR框架和数据分区是评估和分析学生学习成果的宝贵工具。