The PEPS office (Presentation, Events, and Production Support) came to the DataSquad with a problem: It was taking way too long to schedule their student-workers. These workers are needed to check technology around campus – think projectors, touch panels, document cameras, and CD players – but given that work-schedules vary and rooms were occupied during set times, scheduling workers manually was incredibly time-consuming.
With over a hundred classrooms, performance spaces, and meeting rooms around campus to check (referred to collectively as “presentation spaces”), it was time to automate this process. In came DataSquad’s own Auiannce Euwing ‘26, who developed code to automate the technology-check scheduling process. At the start of Auiannce’s project, this task seemed straightforward, but many questions quickly arose such as:
- Should certain presentation spaces be prioritized?
- When are spaces unoccupied?
- How long is each shift?
- Should student-workers only check spaces that are close in proximity?
The final question was especially difficult to implement, but nonetheless crucial for a successful solution. According to Auiannce, it was essential to account for distance between spaces:
Students can only realistically cover classrooms that are close together during their shift. That meant I had to figure out distances between buildings and limit assignments so no one spent their whole shift walking.
After many attempts to solve the question of distance – one of which used latitude and longitude coordinates to calculate distance between spaces – a practical solution emerged: dividing presentation spaces into zones.
Based on both distance and amount of presentation spaces in each building, four zones formed. These zones can be seen in the orange, green, red, and blue colored buildings in Fig. 1. Some buildings, like the Multicultural Center in 2G, only have one space to check, while others like Anderson Hall in F5, have fourteen. The PEPS office, the client for this project, is located in J2.
To incorporate these zones into the larger assignment process, student-workers could only be assigned presentation spaces in one of the four zones. This minimized travel time as much as possible.
Zones on campus were just one piece of the puzzle. For the overall design, it took many iterations to develop a complete, programmatic solution. As Auiannce reflects on the iterative design process, she notes the importance of small improvements over time:
My first versions weren’t perfect, but they worked. Once I had something running, I could slowly make it faster and smarter.
Eventually, a complete solution was ready; it utilized a complex flow (Fig. 2) that optimized technology-check assignment.
Ultimately, this project reflects the purpose of the DataSquad: It solved a complex problem for the PEPS office, while providing valuable, practical experience for DataSquad employees. With the power of code, a manual task was automated, saving time during every use.
Thanks for reading! If you want, take a look at the python code for yourself! It can be found on Auiannce’s GitHub, https://github.com/auiannce/Automating-Classroom-Check/tree/main.
