Extracting Rules from Event Data for Study Planning

TL;DR

This paper uses process and data mining on campus event data to analyze students' study paths, generating data-driven rules to improve study planning and academic success.

Contribution

It introduces a method to extract actionable study planning rules from course sequence data using decision trees.

Findings

Course sequence features explain academic performance

Rules improve understanding of study success factors

Potential for more adaptable study plans

Abstract

In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guidance for their study planning. We employ process and data mining techniques to explore the impact of sequences of taken courses on academic success. Through the use of decision tree models, we generate data-driven recommendations in the form of rules for study planning and compare them to the recommended study plan. The evaluation focuses on RWTH Aachen University computer science bachelor program students and demonstrates that the proposed course sequence features effectively explain academic performance measures. Furthermore, the findings suggest avenues for developing more adaptable study plans.