Abstract
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.
Highlights
COLLEGE years are usually referred to as “the best part of one’s life,” as far as these years go, a lot Manuscript received April 30, 2019; revised July 18, 2019 and September 13, 2019; accepted March 11, 2020
We introduce a novel method to mathematically measure the importance of a course with respect to its relative impact on the graduation time
We introduce novel metrics to characterize prerequisite networks based on a data-driven probabilistic student flow approach
Summary
COLLEGE years are usually referred to as “the best part of one’s life,” as far as these years go, a lot Manuscript received April 30, 2019; revised July 18, 2019 and September 13, 2019; accepted March 11, 2020. We analyze curriculum prerequisite networks based on a data-driven probabilistic student flow approach. We characterize university curricula by a data-driven probabilistic student flow approach and determine the expected graduation time by considering both the topology of the prerequisite network and the completion rates of the courses. Weber has developed a decision support system based on discrete-event simulation to help curriculum planners to achieve the maximal success of students [15] Another line of research is to measure curricular complexity and the structure of curriculum prerequisite networks with the tools of the network theory. This article combines curriculum prerequisite network analysis with discrete-event computer simulation modeling by introducing a data-driven probabilistic student flow approach to characterize prerequisite networks. We present a software tool for analyzing prerequisite networks based on our proposed approach and we discuss how it can support a wide range of educational stakeholders such as curriculum designers, administrators, and students
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