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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

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Detailed manufacturing process data and sensor signals are typically disregarded in production scheduling. However, they have strong relations since a longer processing time triggers a change in schedule. Although promising approaches already exist for mapping the influence of manufacturing processes on production scheduling, the variability of the production environment, including changing process conditions, technological parameters and the status of current orders, is usually ignored. For this reason, this paper presents a novel, data-driven approach that adaptively refines the production schedule by applying Machine Learning (ML)-models during the manufacturing process in order to predict the process-dependent parameters that influence the schedule. With the proper prediction of these parameters based on the process conditions, the production schedule is proactively adjusted to changing conditions not only to ensure the sufficient product quality but also to reduce the negative effects and losses that delayed rescheduling would cause. The proposed approach aims on minimizing the overall lateness by utilizing an active data exchange between the scheduling system and the predictive MLmodels on the process level. The efficiency of the solution is demonstrated by a realistic case study using discrete event simulation.

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