This paper presents an approach of using methods of process mining and
rule-based artificial intelligence to analyze and understand study paths of
students based on campus management system data and study program models.
Process mining techniques are used to characterize successful study paths, as
well as to detect and visualize deviations from expected plans. These insights
are combined with recommendations and requirements of the corresponding study
programs extracted from examination regulations. Here, event calculus and
answer set programming are used to provide models of the study programs which
support planning and conformance checking while providing feedback on possible
study plan violations. In its combination, process mining and rule-based
artificial intelligence are used to support study planning and monitoring by
deriving rules and recommendations for guiding students to more suitable study
paths with higher success rates. Two applications will be implemented, one for
students and one for study program designers.Comment: 12 pages, 4 figures, conference, 30 reference