A Proposed Mathematical Optimization Approach for Undergraduate Teaching Assistant Selection and Tutorial Scheduling

What the paper says

Many university courses, especially in science, technology, engineering, and mathematics (STEM) fields, include weekly tutorial sessions, offering students a more interactive and hands-on learning experience. They are taught by teaching assistants, with an increasing trend to consider undergraduate teaching assistants (UTAs). However, challenges hinder tutorial effectiveness. First, UTAs, who are also students, can only apply for teaching tutorials that fit their course schedules. The reduced list of courses that they can apply for may leave the best candidates aside, affecting the effectiveness of the tutorials. Second, as tutorials are not led by professors, they are often scheduled at inconvenient times, potentially increasing absenteeism. To enhance the learning experience of students in tutorial sessions, we propose an administrative framework for the course enrollment and UTA selection processes along with a mathematical optimization model for UTA assignment and tutorial scheduling. This framework allows prospective UTAs to apply for all courses that they want, streamlining the selection process, and the optimization model produces an optimal tutorial schedule that promotes student attendance. We tested our proposed approach on a real case study, resulting in significant improvements over the existing methods. The number of UTA applications increased by 35%, better UTAs were selected, and an improved tutorial schedule was obtained.