Causal Inference in Higher Education: Building Better Curriculums

Abstract

Higher educational institutions constantly look for ways to meet students' needs and support them through graduation. However, even though institutions provide degree program curriculums and prerequisite courses to guide students, these often fail to capture some of the underlying skills and knowledge imparted by courses that may be necessary for a student. In our approach, we use methods of Causal Inference to study the relationships between courses using historical student performance data. Specifically, two methods were employed to obtain the Average Treatment Effect (ATE): matching methods and regression. The results from this study so far, show that we can make causal inferences from our data and that the methodology may be used to identify courses with a strong causal relationship - which can then be used to modify course curriculums and degree programs.