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.
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Acknowledgement
The authors gratefully acknowledge the financial support by the Federal Ministry of Education and Research (BMBF) for the joint project AIStudyBuddy (grant no. 16DHBKI016).
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Rafiei, M. et al. (2024). Extracting Rules from Event Data for Study Planning. In: De Smedt, J., Soffer, P. (eds) Process Mining Workshops. ICPM 2023. Lecture Notes in Business Information Processing, vol 503. Springer, Cham. https://doi.org/10.1007/978-3-031-56107-8_28
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DOI: https://doi.org/10.1007/978-3-031-56107-8_28
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