Extracting Rules from Event Data for Study Planning

arXiv (Cornell University)

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.

Authors 7

1. Majid Rafiei
2. Duygu Bayrak Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science , RWTH Aachen University , Aachen , Germany

3. Mahsa Pourbafrani Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science , RWTH Aachen University , Aachen , Germany

4. Gyunam Park
5. Hayyan Helal Aachen Knowledge-Based Systems Group

   RWTH Aachen University

   Affiliation as printed

   Knowledge-Based Systems Group , RWTH Aachen University , Aachen , Germany

6. Gerhard Lakemeyer Aachen Knowledge-Based Systems Group

   RWTH Aachen University

   Affiliation as printed

   Knowledge-Based Systems Group , RWTH Aachen University , Aachen , Germany

7. Wil M. P. van der Aalst Aachen Chair of Process and Data Science

   RWTH Aachen University

   Affiliation as printed

   Chair of Process and Data Science , RWTH Aachen University , Aachen , Germany