How University Libraries Can Fairly Allocate Study Spaces

3 min

Students are diligently working at their study spots in a library

Transparent Learning Space Allocation in Libraries

In many college and university libraries, the day starts early for students. Those looking for a study spot queue up early in the morning, hoping for an available table, yet often end up empty-handed. At the same time, throughout the day, spots remain unused: reserved but not occupied, marked only by abandoned laptops or bags. Especially during exam periods, this inefficiency in the use of limited study spaces becomes apparent.

Libraries are central places for focused work, group study, and overall academic success. The question of how to allocate limited study spots fairly, transparently, and reliably is therefore even more pressing, ensuring that trips to the library are actually worthwhile.

Digital booking systems can help libraries tackle this challenge in a structured way. They provide planning security for students and enable libraries to implement clear allocation rules. The key is less about merely digitizing the allocation process and more about designing a fair model that considers different user needs and prevents misuse.

Why "fair" is more than "first come, first served"

At first glance, it seems fair to allocate study spaces on a first-come, first-served basis. In practice, however, this model often leads to problems:

Individual students block spaces well in advance.
Others can't find an available spot despite regular use.
No-shows reduce actual occupancy.

Critical in this process are two key factors: the timeline for advance bookings and the limitation of future booked time per person. Additionally, mechanisms are needed to ensure that reserved spaces are either used or released if not occupied.

A fair advance booking period: Balancing planning & availability

The advance booking period determines how many days in advance a study space can be reserved. It thereby defines the timeframe in which bookings are possible at all.

If this period is very short, booking is only possible with a narrow lead time. This increases the pressure during booking: many students simultaneously try to secure one of the limited spots. The queue doesn't disappear but shifts from the library to the computer.

A simplified numerical example illustrates this effect: If a library has 100 study spaces (capacity), which are on average occupied about 1.6 times per day (occupancy factor), with an advance booking period of only one day, there are about 160 bookable time slots. These compete with around 1,000 students, meaning many are competing for a very limited number of slots. This repeats daily, so some students may consistently miss out.

If the advance booking period is extended to seven days, a different picture emerges: With the same capacity, there are theoretically about 1,120 bookable time slots (7 days × 1.6 bookings × 100 spaces). This means more booking opportunities are available than there are potential users, so each interested person can theoretically book a slot.

The number of study spaces stays the same, and the basic scarcity remains. However, it is spread over time: the booking pressure is distributed over several days, instead of focusing on a short lead to increase equal opportunity.

How far in advance study spaces should be bookable depends on the balance between supply and demand. The scarcer the capacity and the higher the demand, the more important a sufficiently long booking lead time is. In many use cases, a period of one week has been established for this, which can be adjusted according to the situation.

A longer booking lead time, however, raises another question: How can it be prevented that a student books multiple slots every day?

Limited Booking Duration for the Future: Ensuring Equal Opportunity

It may seem logical to manage usage through a fixed quota or a limit on the number of hours a person is allowed to book per week or day. However, such an approach alone falls short. Students use libraries very differently: while some work several hours on-site daily, others come only occasionally. A rigid quota wouldn't accommodate these differences.

Therefore, the solution doesn't lie in the quota alone but in the combination with an open booking period in the future. Each person has a fixed hour limit for advance bookings, e.g., 14 hours. Once a booking has occurred, the limit opens fluidly. This way, frequent users can visit the library every day by booking available remaining capacities throughout the week. However, pre-booking all days at once is prevented to allow other students booking opportunities.

A concrete example: Study spaces can be booked up to seven days in advance. For future bookings, each person can reserve a maximum of 14 hours in total. A student books seven hours early for Tuesday and another seven hours for Wednesday. This exhausts her quota for advance bookings. If a table becomes available at short notice on Wednesday for Thursday, she can still book it on Wednesday, because the booking for Tuesday has already taken place. Thus, the fixed quota prevents the long-term blocking of spaces but does not exclude actual usage when capacity is available.

This dynamic principle creates equal opportunities without restricting frequent users: The usage is not limited, only the length of pre-planned bookings is.

On-Site Check-In & Automatic Release

Even a well-balanced allocation model reaches its limits when reserved spots are not actually used. A common issue in libraries is therefore missed bookings: spots remain empty, even though they are officially occupied. Digital check-in mechanisms address this issue precisely and ensure that the previously defined rules are applied in everyday life:

Users confirm their presence on-site within a defined time window via QR code scan or app.
If there is no check-in, the spot is automatically released.
If learning ends early, the spot can be released again by checking out.

The result: higher actual occupancy, less frustration, and fairer use of shared learning spaces — especially during peak times.

Case Study: Fair Study Space Booking at the LMU Munich Library

A vivid example of fair allocation rules is shown by the University Library of the LMU Munich. There, study spaces and workrooms are managed via the central booking system anny, which allows students to reserve spots in advance for different time periods. During examination periods, several thousand bookings are processed daily, distributed among many students and various library locations.

Usage is organized through faculty-specific groups (“Communities”), ensuring that certain quotas remain reserved for the corresponding fields of study, while also allowing generally accessible spots to be bookable. Students appreciate the transparent planning and equal allocation, especially in times of high demand.

The Case Study of the University Library of the LMU Munich provides a deeper insight into the specific implementation.

Conclusion: Fairness arises from rules, not chance

Fair allocation of study spaces isn’t a matter of chance, but the result of clearly defined, transparent rules that can be flexibly adapted to the daily routines of students and libraries. Digital systems, such as anny, provide university libraries with the opportunity to easily implement and further develop these rules as needed.

Especially in a university context, it becomes clear: When planning security, limited advance bookings, and reliable usage are sensibly combined, everyone benefits, from students to library teams.

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