Are you wasting money on empty seats?

By Curtis / May 19, 2020

4 min read

Scheduling is hard. Balancing student satisfaction with a budget is a tricky problem during normal times. We have been working in higher ed. for many years, and as a node between several institutions wanted to share what we’ve seen, as well as what we expect to change in light of recent events.

University schedules were inefficient before the pandemic. They can no longer afford to be.

Pre-pandemic, we had worked with several institutions to help optimize schedules & improve student satisfaction. In our experience, the typical schedule has considerable amounts of curricular waste - defined as instructional capacity which generates no incremental utility for students. We’ve seen institutions able to reduce their capacity by 10-30%, with no negative impact on student’s schedule quality or experience1.

In a post-pandemic world, the stress placed on higher ed. institutions necessitates greater efficiency than ever before. Any resources that can be freed up will allow institutions to better respond to the myriad of coming challenges.

Enrollment decline is coming – and will create substantial challenges in budgeting capacity for next year

With enrollments forecast to decline ~25% next fall2, institutions are faced with the question of how to schedule classes and determine spend on instructional capacity. Historically, registrars would be able to look at the prior year’s data and make intelligent adjustments based on additional admittance, changes to the curriculum etc. This year has no precedent – enrollment is declining, yield is uncertain, and typical scheduling constraints (timing, travel time between campuses) have been lifted due to distance learning.

Incorrectly budgeting instructional capacity is very expensive, and can negatively impact student happiness

With instruction constituting 25-35% of an average institution’s spend per student3, the cost of over-budgeting educational capacity is substantial. With enrollments forecast to decline ~25%, there is an opportunity for institutions to reduce their spend on instructional capacity by the same magnitude. The reduction of capacity is a tricky problem because of the ‘sectional’ nature of schedules. It may be easy to drop a class that offers 4 sections down to 3, but the decision for a class that offers 1 or 2 sections is much more challenging.

On the other side of the coin, under-allocating hinders an already jeopardized student experience. For a student already unhappy with the challenges of distance learning, being unable to enroll in the courses they want or need to take may be the last straw. The result is that most institutions will sacrifice resource efficiency for student satisfaction.

There’s some good news - scheduling with partial or full distance learning models removes typical constraints

Fortunately for registrars, there is some good news. With the shift toward distance learning models, two major scheduling constraints have been relaxed.

  1. Timing of sections is no longer relevant - online courses have fewer live components
  2. Geography is no longer relevant across campuses - students can take classes anywhere and instantly move from section to section

Amidst all the other challenges facing higher ed, institutions would be remiss to not take advantage of more flexible and efficient scheduling options. It’s a tricky optimization problem, but one with significant financial returns for those able to crack it.

The most efficient operations will be built dynamically to adapt based on new information.

The core of the problem here is that demand for courses remains variable & uncertain until students enroll. In a traditional course selection process schedules are generated and then students slot themselves into the different sections - leaving registrars hoping that they properly estimated demand. The solution is to build a dynamic process, where schedules can be adjusted retroactively as the shape of student demand is revealed.

This is the problem we are passionate about solving here at Course Match.

With Course Match, students input their preferences rather than signing up for specific slots. Once preferences are collected, our two-stage optimization algorithm kicks in (for those curious, the whitepaper can be found here)

First - Course Match calculates the demand of all sections, generating ‘demand data’

Second - Course Match optimally allocates classes to students - maximizing student experience

Institutions can then use the demand data to identify classes that have low demand and occupancy, remove them, and reallocate curricular resources to add capacity to classes that students actually want. In short, administrators are able to give students more of the classes they actually want while actually offering fewer seats.

The real question is not if you are wasting money on excess seats, but whether you are offering the right seats at all.

Let’s talk.

We’ve seen incredible collaboration within higher ed. during these times, with 55% of IT leaders openly sharing information with other institutions. It’s more important now than ever before to put our heads together, and we’d love to keep the dialogue going.

If you’re interested in speaking further about any of these topics or learning more about how Course Match can help you navigate the coming challenges please feel free to reach out –


  1. Course Match deployments from 2017-2020 ↩︎

  2. [The American Council of Education (ACE)] ↩︎

  3. [The Delta Cost Project] ( ↩︎

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