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A BUSINESS-ANALYTIC APPROACH TO IDENTIFY CRITICAL FACTORS IN QUANTITATIVE DISCIPLINES

机译:在定量学科中确定关键因素的业务分析方法

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摘要

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.
机译:美国各地大学中的大多数商科学生都发现,以量化为导向的课程对于将课程材料理解到发展解决商务问题的能力和信心水平所必需的程度具有挑战性。确定影响此类课程成绩的关键因素对于设计课堂教学至关重要。讲授这些课程的讲师对以下事实感到苦恼:这些课程属于最难教授的课程,因为它们包含了相对较难的概念,这些概念已转化为分析技能,并实际应用于学生难以掌握的业务运作中。这项研究采用了一种基于机器学习的方法,通过分析重点课程的数据集来确定关键的成功因素,并为教育工作者提供了一些指导方针,以提高他们的教学效果。在数据挖掘模型上基于信息融合的敏感性分析为变量的排名提供了一种无偏加权方案,有助于预测学生的理解水平。

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