Case Study:

Addressing a Frustrating, High-Stakes Process

Class registration has always been a high-stress experience for students. As a designer focused on usability and accessibility, I wanted to understand why. Is it solely a technical issue—server crashes, slow load times? Or is there more to it?

After doing more research, I found a common theme: students were making critical decisions with incomplete or unclear information. They had no way to gauge their chances of getting into a class, no easy way to compare sections, and no system to help them resolve scheduling conflicts in real time. This lack of transparency forced students to rely on guesswork and frantic backup plans, turning what should be a straightforward process into a stressful gamble.

Challenging My Assumptions

Initially, I assumed students primarily struggled with slow systems and complex interfaces. But after conducting user research, I realized the real pain points were more nuanced:

• Students didn’t just want to register quickly—they wanted confidence in their decisions.
• Visibility into registration odds was more valuable than just seeing if a class was “open” or “full.”
• Comparing sections was clunky—students were manually flipping between pages to make decisions.
• Scheduling conflicts weren’t obvious enough and this lead to last-minute scrambles.

These insights completely reframed my approach. It wasn’t just about making registration faster—it was about making it predictable and stress-free.

Exploring Solutions

With these insights in mind, I started sketching out ideas. I leaned into data visualization to surface key information at the right moments:

• A simple percentage-based indicator to show registration chances at a glance.

• A block-based schedule view that made conflicts visually obvious.

• Inline section comparisons so users can compare sections.

• Phased registration waves to ease server load and to create a more equitable sign-up process.

Cutting Through the Noise

One thing that I kept thinking about throughout this project was how much data was too much. Early on, I experimented with adding GPA distributions and pass rates alongside registration chances. But through user testing, I discovered something surprising—most students didn’t find this useful. They weren’t trying to analyze success rates; they just wanted to know, "Can I get into this class?"

Before Testing

After

This was a key moment in the project. Instead of overwhelming students with every available metric, I refined the experience around only the most actionable data. This decision simplified the UI and aligned directly with what users needed most.