Data driven course scheduling to ensure timely graduation

Ming Chen, Xuan Huang, Hongyu Chen, Xuemei Su and Jasmine Yur-Austin

International Journal of Production Research, 2023, vol. 61, issue 1, 336-361

Abstract: With progressively decreasing state funding in the last two decades, timely graduation has become an imperative yet challenging problem for many public universities. Our research empirically studies students' enrolment and performance data during an 8-year period in a large college at the California State University Long Beach. Through data analytics, we identify four fundamental issues that lead to delayed graduation. We propose innovative solutions that directly tackle each of the four identified issues while systematically matching capacity and demand. Specifically, we propose major-specific degree roadmaps tailored to increase the chance students can successfully complete all required courses within the timely graduation window. Given major migration behaviours, we design robust roadmaps that proactively prepare students for possible major change later without delaying graduation. The well-crafted degree roadmap provides students with a clear path to degree attainment, as well as guidance on the timing of course enrolments. Further, to maximise students' access to courses as well as capacity utilisation, we develop an optimisation model to determine the class schedule in which all students are guaranteed a seat within their preferred time window in all required classes. The proposed approach is widely applicable to many institutions facing the timely graduation challenge.

Date: 2023
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DOI: 10.1080/00207543.2021.1916118

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