High-Precision, Fair University Course Scheduling During a Pandemic
Matthew E. H. Petering, Mohammad Khamechian

TL;DR
This paper presents a novel, fair, and high-precision university course scheduling algorithm that ensures significant in-person instruction during a pandemic, accommodating reduced classroom capacities and enabling quick deployment.
Contribution
It introduces an expanded taxonomy of delivery modes, an integer programming model, and a heuristic algorithm for fair, simultaneous attendance scheduling during pandemics.
Findings
At least 25% of instruction remains in-person even with 75% capacity reduction.
The approach covers over 49% of total instruction campus-wide during pandemic constraints.
The scheduling algorithm operates within an hour, enabling rapid deployment.
Abstract
Scheduling university courses is extra challenging when classroom capacities are reduced because of social distancing requirements that are implemented in response to a pandemic such as COVID-19. In this work, we propose an expanded taxonomy of course delivery modes, present an integer program, and develop a course scheduling algorithm to enable all course sections -- even the largest -- to have a significant classroom learning component during a pandemic. Our approach is fair by ensuring that a certain fraction of the instruction in every course section occurs in the classroom. Unlike previous studies, we do not allow rotating attendance and instead require simultaneous attendance in which all students in a section meet in 1-5 rooms at the same time but less often than in a normal semester. These mass meetings, which create opportunities for in-person midterm exams and group…
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Taxonomy
TopicsOnline Learning and Analytics
