Extracting Rules from Event Data for Study Planning
Majid Rafiei Duygu Bayrak Mahsa Pourbafrani Gyunam Park Hayyan Helal Gerhard Lakemeyer Wil M. P. van der Aalst
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.