疫情期间高精度、公平的大学课程排课
High-Precision, Fair University Course Scheduling During a Pandemic
AI总结：
本研究针对疫情下教室容量缩减的大学排课难题，提出含扩展授课分类、整数规划与快速启发式算法的公平同步出勤排课方案，可在容量降75%时保障每班至少25%线下教学，兼顾精度与效率，适用于疫情及常规时段。
摘要：
当因应对COVID-19等疫情实施社交距离要求导致教室容量缩减时，大学课程排课面临额外挑战。在本研究中，我们提出了一套扩展的课程授课模式分类法，构建了一个整数规划模型，并开发了一种课程排课算法，以确保所有课程班型——即使是规模最大的班级——在疫情期间仍能保有显著的课堂学习组成部分。我们的方法具备公平性，确保每个课程班型中都有一定比例的教学活动在教室内开展。与以往研究不同，我们不采用轮换出勤模式，而是要求同步出勤：即一个班级的所有学生同时在1至5间教室内上课，但上课频率低于正常学期。这些大规模集会为线下期中考试和小组活动提供了机会，其排课以高精度覆盖整个学期的所有天数，而非仅在单一重复周内安排。一种快速启发式算法可在1小时内生成排课方案。实验结果：我们以[XYZ大学]2022年秋季学期的1834个线下课程班型、172间教室和96个教学日为研究场景。若疫情导致教室平均容量缩减75%，我们的方法仍能保证每个班型至少25%的教学活动线下开展，整个校园的全部教学活动中超过49%可在教室内进行。我们的方法在常规教室分配任务中也能产出优异结果。管理启示：基于公平性和同步出勤原则的算法，能够在疫情期间及常规时段显著优化大学课程排课。可为校园应对各类疫情可能性做好准备的高精度排课方案，仅需最少的行政工作量即可生成，且能在学期前或学期中疫情暴发时即刻启动启用。
英文摘要：
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 activities, are scheduled at high precision across all days of the semester rather than a single, repeating week. A fast heuristic algorithm makes the schedule in an hour. Results: We consider the 1834 in-person course sections, 172 classrooms, and 96 days in the fall 2022 semester at [UniversityXYZ]. If average classroom capacity is reduced by 75% due to a pandemic, our approach still allows at least 25% of the instruction in every section, and more than 49% of all instruction across the entire campus, to be in the classroom. Our method also produces excellent results for regular classroom assignment. Managerial implications: An algorithm based on the principles of fairness and simultaneous attendance can significantly improve university course schedules during a pandemic and in normal times. High-precision schedules that prepare a campus for various pandemic possibilities can be created with minimal administrative effort and activated at a moment's notice before or during a semester if an outbreak occurs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
