University system & multiple enrollment policy: dropout and graduation clusters

Abstract

Dropout of university students can have negative consequences for students’ futures as well as for university income. University systems with a multiple enrollment policy may offer students benefits, such as a second chance to graduate instead of dropping out. However, little is known about the outcome constellations (graduation, dropout) of undergraduate students in such a university system. Austria’s (Europe) university system has no tuition fees, but has introduced student performance regulations based on student workload and study duration. Students have to fulfill certain performance goals within a predefined period to not lose their enrollment to a program. Therefore, this study aims to cluster different groups of students based on their outcome constellations of dropout and graduation in a university system allowing multiple enrollments. The sample consisted of 59,574 students in 93,969 bachelor’s and diploma programs of an Austrian university between 2010/11 and 2022/23. The data was retrieved from the administrative database of the University of Graz. Workload and study duration were used as the major outcome dimensions. Cluster analyses with four algorithms were calculated per outcome constellation, the results were categorized and aggregated in matrices for workload (low, medium, high) and duration (low, medium, long). Having more than one enrollment can be associated with a higher workload and longer overall study duration. There are student groups graduating from one program and dropping out of another, representing those that get a second chance for a degree or those that strategically switch programs. A multiple enrollment policy can give students the opportunity to try out different programs and to fail, since they can choose a program that fits them best to finally graduate. The results can aid policymaker’s decisions around multiple enrollment policies, considering possible advantages and disadvantages for students and institutions.

Keywords

• Academic analytics
• cluster analysis
• multiple enrollment
• parallel enrollment
• student retention
• Education Policy