Before each academic term, students at the University of Toronto are responsible for designing their own timetables. However, even when they have the courses they need to enroll in within their grasp, it may turn out to be a struggle to pick the most befitting lecture sections so that the classes are optimally scheduled. For commuters, amateur athletes and students with other occupations, it is particularly a relevant issue since they have more other activities to keep in harmony with their university classes, hence a greater need for a well-organized schedule.
In addition to that, not all aspects of the UofT’s current course-enrolment software, ACORN, are ideal. For instance, based on the long-standing ‘UofT Time’ system, all classes start ten minutes after the hour and end right on the hour. This gives students a 10-minute commute window between the lecture buildings so that they can attend the entirety of their classes even if the lectures are scheduled back-to-back. But is, in fact, the walking distance between every two buildings at UofT under 10 minutes? While the answer to this is clearly “no”, does ACORN caution students when they enroll in two consecutive lectures happening in buildings that are more than 10 minutes apart? Or do students have to manually go on to Google Maps and check the walking time between their successive lecture locations by themselves? Unfortunately, the latter is the case at present.
With all these nuisances in mind, observing the necessity for a more facilitated and convenient planning tool, we decided to focus our project on the following research question:
Using a tree-like data structure, how can an AI design the most suitable timetable for UofT students given their availability and the courses they want to enroll in?
As such, our initiative was to develop a software that helps students, upon specifying which courses they are interested in taking in which term, obtain the most time-efficient schedule at the UofT. By doing so, not only did we make it possible to generate timetables that have at most 10 minutes of traveling distance between the locations of back-to-back lectures, but we also made sure that our product provides the most flexible time-schemes to its users within the frame of their availability. In particular, we programmed our system to produce timetables that keep the user’s day in the university campus as short as possible while ensuring that they have enough breaks between the classes, so as not to get exhausted along the way. By means of this project, we aimed to assist the UofT students in their time-management skills and have them avoid undesired frustration, arising especially amongst first-year students unfamiliar with the campus setting.