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.

中文翻译：

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通过学生流方法表征课程必备网络

课程先决条件网络在塑造大学课程过程中起着核心作用。由于设计一个合适的网络在学术和经济上都非常重要，因此对前提网络的分析近来引起了很多研究兴趣。它决定了程序的学习目标，并且对完成时间和辍学有很大影响。在本文中，我们介绍了一种数据驱动的概率学生流方法来表征必备网络，并基于网络拓扑和课程的完成率研究毕业时间的分布。我们还提出了一种方法来确定对毕业时间有重大影响的课程。我们的学生流程方法还能够模拟政策变更和网络修改的影响。