# A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education

arXiv: https://arxiv.org/abs/2211.12190 · HTML: https://mlsanity.com/paper/2211.12190/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 22 Nov 2022.

## Builds on (most cited first, 1 of 1)

- [Conformance Checking Based on Multi-Perspective Declarative Process Models](https://mlsanity.com/paper/1503.04957/) (2015)

## Built on by (most cited first, 3 of 3)

- [Gaining Insights into Group-Level Course Difficulty via Differential Course Functioning](https://mlsanity.com/paper/2406.04348/) (2024)
- [Reasoning about Study Regulations in Answer Set Programming](https://mlsanity.com/paper/2408.04528/) (2024)
- [The Course Difficulty Analysis Cookbook](https://mlsanity.com/paper/2508.13218/) (2025)

## Abstract (arXiv)

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.