A Set Union Based Formulation for Course Scheduling and Timetabling

The Course Timetabling Problem is a widely studied optimization problem where a number of sections are scheduled in concert with the assignment of students to sections in order to maximize the desirability of the resulting schedule for all stakeholders. This problem is commonly solved as a linear program with variables for each student or group of students with identical schedules. In this paper we explore an alternative formulation that aggregates binary student variables into integer variables denoting the number of students enrolled in a course. Our solution method assumes decomposition of the general schedule into time blocks, and applies a unique set theory based, integer linear programming formulation that seeks to maximize the total number of students enrolled in their desired sections across the time blocks. Once the problem has been solved, the simpler problem of disaggregating the solution is resolved. This approach can be used to find exact solutions, given sufficient computing power, or simplified to quickly find solutions within calculable bounds of optimality. Case studies with a local elementary school and a local high school show that the new formulation is significantly faster and can be made to be reasonably accurate

An examination of student outcomes in studio chemistry

Twenty years ago, a major curriculum revision at a large, comprehensive university in the Western United States led to the implementation of an integrated lecture/laboratory (studio) experience for our engineering students taking general chemistry. Based on these twenty years of experience, construction of four purpose-built studio classrooms to house the majority of the remaining general chemistry courses was completed in 2013. A detailed study of the effects of the entire ecology of the studio experience on student success was initiated at that time. Data from content knowledge pre- and post-tests, learning attitudes surveys, and student course evaluations show positive effects on student performance, the development of more expert-like learning attitudes, increased student engagement, and increased student–instructor interactions vs. the previous separate lecture and laboratory instruction for non-engineering students. Our data also show that an associated new peer Learning Assistant program increases student engagement while also having positive impacts on the Learning Assistants themselves