A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education
This addresses study planning inefficiencies for students and program designers in higher education, but it appears incremental as it combines existing methods in a new application.
The paper tackles the problem of analyzing and optimizing student study paths in higher education by combining process mining and rule-based AI to detect deviations and provide recommendations, aiming to guide students to more suitable paths with higher success rates.
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.