This project explores how process mining can be applied to understand and improve student progression pathways through university curricula. Using Celonis, a leading process mining platform, the project will analyse enrolment and performance data from the UNSW Master of Information Technology (MIT, program 8543) as a case study.
Students often follow diverse journeys through a program, influenced by prerequisites, course availability, and personal choices. By transforming academic records into event logs and applying process mining techniques, this project aims to uncover actual progression patterns, identify bottlenecks (e.g., frequently repeated courses or delayed electives), and compare them to the intended curriculum structure.
School
Computer Science and Engineering
Research Area
Process mining | Process analytics | Educational data mining | Curriculum studies
Suitable for recognition of Work Integrated Learning (industrial training)?
No
- Research environment
- Expected outcomes
- Supervisory team
- Reference material/links
Software/Tools:
- Celonis Academic Edition (provided free for educational research).
- Supporting Python libraries (e.g., PM4Py, Pandas) for data preprocessing.
Data:
- Synthetic or de-identified enrolment records reflecting UNSW MIT program structures (such as core/elective courses, prerequisites, and grades).
- Curriculum structure from the official UNSW Handbook.
- A Celonis process model showing actual versus intended student pathways in the MIT program.
- Insights into bottlenecks, common alternative sequences, and risky transitions (e.g., failing a core course or delaying electives).
- A short research report (15–20 pages) summarising methods, results, and recommendations.
- A visual demo dashboard in Celonis that showcases pathway analysis for teaching, advising, or curriculum planning.
- Van Der Aalst, W. M., & Carmona, J. (2022). Process mining handbook (p. 503). Springer Nature.
- Celonis Academic Alliance https://www.celonis.com/company/our-programs/academic-alliance