Identifying Preparatory Courses that Predict Student Success in Quantitative Subjects

Abstract

College courses are often organized into hierarchical sequences, with foundational courses recommended or required as prerequisites for other offerings. While the wisdom of particular sequences is usually ascertained on the basis of faculty experience or student peer networks, machine learning techniques and ubiquitous transcript data make it possible to systematically identify the courses that best predict subsequent high achievement across entire curricula and student populations. We demonstrate the utility of this approach by analyzing five years of course sequences and earned grades for 13,218 undergraduates enrolled in courses with substantial quantitative content at a private research university. Findings indicate that prior completion of specific courses is positively associated with success in subsequent target courses, and suggest that academic planning could be enhanced through scaled observation of the revealed benefits of course sequences.