Characterizing Curriculum Prerequisite Networks by a Student Flow Approach

Curriculum prerequisite networks have a central role in shaping the course of university programs. The analysis of prerequisite networks has attracted a lot of research interest recently since designing an appropriate network is of great importance both academically and economically. It determines the learning goals of the program and also has a huge impact on completion time and dropping out. In this article, we introduce a data-driven probabilistic student flow approach to characterize prerequisite networks and study the distribution of graduation time based on the network topology and on the completion rate of the courses. We also present a method to identify courses that have a significant impact on graduation time. Our student flow approach is also capable of simulating the effects of policy changes and modifications of the network. We compare our methods to other techniques from the literature that measure structural properties of prerequisite networks using the example of the electrical engineering program of the Budapest University of Technology and Economics.