Master's programs’ dropout and graduation clusters in a university system with a multiple enrollment policy

Abstract

Austria (Europe) allows students to enroll in multiple degree programs simultaneously or consecutively. On the master's degree level this may offer advantages for students, such as a chance for graduation instead of dropping out. This study aimed to identify groups among master's students’ outcomes (graduation, dropout), considering the possibility of enrolling in multiple programs. Data from 21,412 master's degree students between 2010/11 to 2022/23 were used as a base for analyses. All possible outcome constellations of dropout and graduation have been considered and the dimensions workload and overall study duration in semesters were used for the clustering. Cluster analyses per outcome constellation, set up on the four algorithms kmeans, clara, pam and hclust, revealed differently sized clusters. Clusters were categorized into low, medium, high workload and low, medium, long duration and their frequencies were aggregated. The results suggest that enrolling in multiple programs increases overall study duration and student workload, but also that groups of students with reduced workload exist, possibly profiting from multiple enrollments. Future studies are needed to examine knowledge transfer between programs and to obtain information on differences between single-enrollment students and multiple-enrollment students in their performance in the job market.