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Partitioning Students into Cohorts During COVID-19

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Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12735))

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Abstract

The COVID-19 pandemic has forced educational institutions to make significant changes to safeguard the health and safety of their students and teachers. One of the most effective measures to reduce virus transmission is partitioning students into discrete cohorts.

In primary and middle schools, it is easy to create these cohorts (also known as “learning groups”), since students in each grade take the same set of required courses. However, in high schools, where there is much diversity in course preferences among individual students, it is extremely challenging to optimally partition students into cohorts to ensure that every section of a course only contains students from a single cohort.

In this paper, we define the Student Cohort Partitioning Problem, where our goal is to optimally assign cohorts to students and course sections, to maximize students being enrolled in their desired courses. We solve this problem by modeling it as an integer linear program, and apply our model to generate the Master Timetable for a Canadian all-boys high school, successfully enrolling students in 87% of their desired courses, including 100% of their required courses. We conclude the paper by explaining how our model can benefit all educational institutions that need to create optimal student cohorts when designing their annual timetable.

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Acknowledgments

The authors thank the reviewers for their insightful comments that significantly improved the presentation of this paper. The authors also thank the administrators at St. George’s School for making this collaboration possible. Specifically, we acknowledge Sarah Coates (Associate Principal of Academics), Andrew Shirkoff (Director of Risk Management), Jan Chavarie (Head of Applications Support), and Jessie Bahia (Registrar).

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Authors and Affiliations

  1. Northeastern University, Vancouver, BC, Canada

    Richard Hoshino

  2. Quest University Canada, Squamish, BC, Canada

    Irene Fabris

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  1. Richard Hoshino
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  2. Irene Fabris
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Corresponding author

Correspondence to Richard Hoshino .

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Editors and Affiliations

  1. Monash University, Melbourne, VIC, Australia

    Peter J. Stuckey

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Hoshino, R., Fabris, I. (2021). Partitioning Students into Cohorts During COVID-19. In: Stuckey, P.J. (eds) Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2021. Lecture Notes in Computer Science(), vol 12735. Springer, Cham. https://doi.org/10.1007/978-3-030-78230-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-78230-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78229-0

  • Online ISBN: 978-3-030-78230-6

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