The Use of Educational Process Mining on Dropout and Graduation Data in the Curricula (Re-)Design of Universities

Abstract

High college dropout rates are not a desired outcome for university management. Efforts have been made to increase student retention via understanding dropouts and building support mechanisms. With the emergence of Big Data, educational process mining came into existence, allowing for new methods of structuring and visualizing data. Previous studies have established an approach to generate process maps from the course sequences students take. This study improves this method by focusing on visualizing students’ pathways through a study program dependent on their status as a “dropout” or “graduate” and on the level of every degree program. An interactive framework in a web application dedicated to curriculum designers was created. The data of 53,839 students in 78,495 studies at the University of Graz (Austria) between 2012/13 and 2022/23 were used for process mining. The generated process maps provide information on the exam sequence of students. They have been implemented in discussion forums with stakeholder groups and are part of the curriculum (re)design processes. The maps provide the benefit of being able to compare and monitor successful and non-successful students’ maps using real-time data. Despite their use for curriculum development, they are limited in their size and the number of exams that can be displayed, making them a good fit for early dropout evaluation.

Keywords

• educational process mining
• dropout
• curriculum design
• academic analytics
• student retention