Is Classroom Scheduling Software for Higher Education Dead?

Outdated scheduling tools often make it difficult for institutions to address strategic challenges, such as space optimization. Learn how your current scheduling processes may be getting in the way of effective resource allocation.

Countless universities believe that the reason why they are running out of space for scheduling classrooms on campus is because they don’t have an effective classroom scheduling software tool. But at the core, their problems truly begin with the other scheduling processes, like instructor scheduling and time scheduling.  

Legacy Solutions Don't Work

Dating back to the 1990s, universities have turned to solutions such as CollegeNET or Ad Astra to provide classroom optimization software. The premise of these tools was to optimize the use of any central classroom space that was owned by the university, and to allow for manual booking of additional rooms.  

These scheduling solutions caught on like wildfire for large universities where the Registrar’s Office owned a large percentage of the rooms. However, in many cases, the number of rooms was quite inadequate. Many folks realized that the notion of running an optimizer and magically building the perfect room assignments rarely came to fruition. Often, there was a huge amount of grunt work to set up these tools: the optimizer would have to be run many times, the administrator would have to go back to departments and negotiate about room assignments, and any rooms that were left for department scheduling were still scheduled quite inefficiently.

Processes Impact Quality of Optimization

While the idea of an optimization is right, the inputs are wrong. At most educational institutions, departments go to their instructors and ask what courses and what times they are going to teach. This often results in many instructors packing themselves into prime-time hours—between the hours of 10 am and 2 pm (or around then) at most campuses. Naturally, this created inordinate pressure on the optimizer to make placements in cases where it might not even be feasible. For example, at one large University of California campus, running an optimizer would regularly result in 30+ bottlenecks, resulting in a time-intensive, political negotiation among the faculty members about who would get the rooms and who would be shifted.

Best Practices Can Make a Difference

Our solution is to focus on cleaning up the inputs, so that the optimizer could actually make effective placements and class schedules. That really starts with instructor preferences: whereby instructors at most campuses tell department schedulers or the Registrar’s Office what they want to teach and when. Interestingly, our internal data suggests that the way in which folks are asked for their preferences has a huge impact on the way room assignments are made. For example, if a department scheduler simply asks what time you would like to teach, the number of acceptable assignments is a lot smaller than if one were to ask the inverse question: “What times can you not teach?” Why does this matter? It results in significantly more room bottlenecks and more after-the-fact change requests when you are unable to shuffle faculty members before the optimizer to make it all work.