Find a Study Space

Conducted UX research to help design a web-based tool that makes it easier for students to find study spaces that match their needs.

Role

Timeline

UX Research Assistant

UX Design Assistant

3 months

Methods

Interviews

Card Sorting

Prototyping

Students couldn’t easily find study spaces that fit their needs, even though U-M Library had hundreds of options.

Problem Statement

Old version of the page, before any redesign work happened. Only bookable rooms were showcased.

Core Challenge

Design a tool that helps students find library study spaces that fit their needs.

Context

At the University of Michigan Library, students often struggled to find study spaces that matched their needs, despite there being many spaces available across campus. During the pandemic, this challenge became even more pronounced as access and discovery were limited. Students were looking for spaces with specific qualities — like quiet, privacy, lighting, or technology — but the library website didn’t make it easy to compare or discover spaces based on those preferences.

U-M Library has lots of study spaces across multiple libraries.

No centralized, up to date inventory describing the spaces in a way the students could understand or compare.

It's the pandemic recovery period. Students aren't doing the usual thing: discovering spaces by wandering.

Frustrated students confused about where to study and underused spaces.

Our team wanted to build something like Cambridge’s SpaceFinder — a way for students to filter by the features they actually care about (privacy, noise level, lighting, etc.) and discover spaces that matched those preferences.

Methods

Subject Matter Expert Interviews + Benchmarking

I investigated what other university libraries were already doing. I also met with the UX researcher at Cambridge who built their SpaceFinder. This helped us avoid reinventing the wheel, understand what design patterns worked elsewhere, and recognize early on that taxonomy is everything — if the categories don’t make sense, students won’t use the tool.

Key Takeaways

Filtering as an effective solution to large inventories.

U-M has over 6 libraries and countless study spaces. Filtering isn't only a search tool. See Hick's Law

Importance of student driven filters

Study space features should align with how students actually think about and search for spaces. See mental models.

For students, it's all about the vibe

Background research revealed students prioritize atmosphere when considering where to study. Students also care about amenities of the space.

Why this mattered: This meeting shaped our approach and confirmed that ultimately, we wanted to create a filter tool. It gave us clear next steps.

Comprehensive Space Audit (Objective Data Collection)

I visited six of our libraries and every study space across the library system. I documented features of the space, such as furniture, privacy, tech, layout, noise level, lighting, etc.

Why this mattered: We needed to create an inventory of all of the amenities and features of our spaces for our future filters.

Two screenshots of some of the data I collected about our study spaces.

Intercept Interviews to Understand Student Vocabulary

I talked with about 25 students to capture how they describe study spaces, so that we could create filters that matched their mental models. Some spaces were described as "cozy" and "inspiring" while others were described as "private", "focused", and "utilitarian".

Why this mattered: The way students describe spaces is not always how staff describe them. We needed student vocabulary to build a language students would actually understand in a filter tool. This resulted in a student-derived word bank.

Card Sorting to Map Vocabulary → Actual Spaces

We had ~35 participants do a card-sorting activity where they matched descriptors (from the interviews) with specific study spaces (from the audit).

Why this mattered: It showed us patterns like which rooms are perceived as “quiet” vs “noisy” what “cozy” really means to students which features students consistently associate with different kinds of spaces. This gave us the bones of a taxonomy we could use in a future interface.

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Three examples from our card sorts with students.

Screenshot with some of the results from the card sorting.

Conclusion

Actionable Insights

Students have their own descriptors for study spaces

Derived from intercept interviews → word bank → card sorting. Original site didn’t match student thinking.
Specific descriptors for spaces captured in card sorting.

Students prioritize atmosphere when it comes to choosing study spaces

Amenities and other features are secondary.
Prototype should capture atmosphere of the space.

Vocabulary alignment is essential

Filters or labels must use student language to be intuitive and accurate.
Student derived word bank should be used to reflect student language

Objective + subjective mapping is critical

Audited features + student language → usable taxonomy for filtering.
Continue validating which attributes students care about most for future iterations.

Low Fidelity Prototype Using Actionable Recommendations

This early prototype demonstrates how our insights informed the design:

Students can browse and compare spaces based on the features they care about most.
Layout emphasizes discoverability, making it easier to scan options without being overwhelming.
Filters would reflect student vocabulary (e.g., “cozy,” “focused,” “inspiring”).

Even at a low-fidelity stage, this prototype serves as a foundation for usability testing and refinement. It provides a visual framework to translate research into design decisions.

Future Directions

Conduct usability testing with students using the prototype to validate whether filters and labels align with their expectations.
Refine the taxonomy of features based on feedback, removing low-priority items and possibly adding new descriptors.
Develop a higher-fidelity version with visuals, study space photos, or live filtering functionality.
Integrate analytics to track which features students use most.

This project showed me how important it is to base design on how real people actually think and behave. The audit gave helpful data, but talking to students early and often made sure the final tool will be intuitive, helpful, and match the way students actually pick their study spaces.