AASCU Edge
Lessons Learned: What One Year of Student-Centered Course Scheduling Taught Us
For most colleges and universities, the course schedule is treated as back-office logistics. A growing body of research suggests course scheduling is a lever for student success, rather than a puzzle to be solved each term. For many students, especially those balancing work, caregiving, and tight finances, the schedule itself can be the difference between staying enrolled and stopping out.
That’s the premise behind AASCU’s Student-Centered Course Scheduling initiative. With funding from Ascendium Education Group, AASCU partnered with Ad Astra, a firm specializing in academic scheduling data, to give 20 regional public universities the tools, data coaching, and peer network to rebuild how they create their schedules.
The work builds on an earlier 11-institution pilot conducted from 2022–24, which found that even modest scheduling changes could measurably shorten students’ path to a degree. Over the past year, a cohort of institutions worked through a simple but revolutionary question: What would it take to build a course schedule around what students need?
1. Getting the schedule right takes shared understanding and a clear process.
When teams first sat down to diagnose scheduling challenges, many expected to find a technical problem: outdated software, missing reports, not enough classrooms, etc. However, that’s rarely what they found.
Scheduling outcomes are the accumulated result of habit and precedent rather than a shared, explicit picture of what students need. That doesn’t mean the fix is purely cultural, though. Clear structure for who decides what, why, and when matters just as much. Without agreement on what the schedule is trying to achieve, new processes tend to fall back into old habits. Without workable processes, good intentions don’t reliably turn into different outcomes. The two need to move together.
Go deeper:
Hear how cohort institutions made the case for building that shared foundation.
2. Student-centered decisions take a whole-system view.
On most campuses, scheduling authority sits with faculty, chairs, deans, registrars, advisors, and provosts. They each play an integral role in creating a student-centered schedule with a unique perspective on a student’s journey. And each of them can reasonably say no to a scheduling proposal given their role and perspective—without seeing how all of those decisions add up for a student trying to build a workable schedule.
The cohort’s language on this idea sharpened over the year, landing on a simple distinction: What’s needed isn’t less authority, but “less shared veto power, more shared responsibility,” as one participant put it. The goal isn’t to strip departments of their say, but rather to pair that authority with visibility into its downstream effects.
Go deeper:
Explore how to build that shared visibility with our recent webinar, Communicating to Support Adoption, Culture Change, and Impact.
3. Test the system by mapping one action, from decision to follow-through.
Decision-mapping is an exercise that asks teams to pick one decision that consistently feels hard or contentious, then trace its parts: who owns it, what data feeds into it, and who feels it first when it goes wrong.
Teams that picked something seemingly small—like who can cancel a low-enrolled section—often found that the honest answer to “who owns this?” was unclear.
Waitlists became a recurring example. Several institutions realized they had no shared definition of what a waitlist is for: a demand signal, a preference signal, or simply a queue. That meant the same data was being read differently by different people across the system, as one cohort participant found firsthand: “It’s enlightening to take one ‘small’ thing and follow it backward and forward to see how we’re creating inconsistency at best and chaos at worst for students.”
Go deeper:
See how to turn a routine decision like this into a testable question in Using Data to Drive Decisions.
4. Translating data is the hard part, not collecting it.
“We need better data” is a common starting point on many campuses, but it’s rarely the true challenge. As Laura Hunter of Ad Astra put it during AASCU’s webinar on data-driven decision-making, most institutions already have data available to them. The harder part is “knowing what the data means, how to interpret it, and how to connect it to your goals.”
A second gap shows up once the first is solved: turning insight into a decision someone else can act on, without it landing as an accusation. One cohort participant suggested a reframe. Rather than “just sending raw data,” which “can be confusing and unhelpful,” leverage data as a tool meant for real use and present it “in an easy-to-understand format” for constituents across campus.
Go deeper:
Start by using the course scheduling data guide.
5. Having enough space is not actually the problem.
When institutions report not having enough space, the data often tells a different story. What’s frequently happening instead is compression. For example: Everyone prefers to teach mid-morning Tuesday and Thursday, so the campus runs at capacity for a narrow window each week and sits underused the rest of the time.
That distinction matters because the two problems come with very different price tags. One calls for capital construction, the other for a conversation about the meeting-pattern grid or how classroom space is scheduled.
As Hunter put it, having worked with more than 500 institutions on this question, almost every campus says it’s out of space, but “the data doesn’t bear that out, because it’s not true across the board. Usually there’s something else at play.”
Go deeper:
Learn the capacity metrics that can help tell the two apart.
6. First-year courses often carry a disproportionate share of scheduling pressure.
Across the cohort’s 16-institution benchmark, one pattern stood out as both consistent and distinctive: First-year courses are more overloaded than the same institutions’ overall course portfolios and external comparison groups. Eight of 16 institutions have more than 45% of first-year courses overloaded.
That matters because first-year courses are often foundational and prerequisite coursework. It’s also worth naming a nuance: Overload and underutilization can coexist at the same institution. Five institutions have more than 40% of courses underutilized—a reminder that this isn’t necessarily a story about offering too few courses. It can be a story about offering the wrong ones in the wrong places
Go deeper:
Learn how to engage students in these conversations; they’re exactly who to hear from first.
7. A little pressure can show people what data often can’t.
The pressure test is a simple exercise: Get faculty, advisors, and administrators in a room, pull up a degree map and the published schedule, and try to create an optimal student schedule for the next two terms.
Most groups don’t get through even one. As Hunter described it, this exercise is “incredibly powerful in helping your stakeholders see the impact of the course schedule on student success outcomes.” What makes it effective isn’t the finding. It’s the moving through the registration experience, using the institution’s own materials and processes.
Go deeper:
Hear more about the exercise and how to make the case for scheduling reform.
8. Registration data tells you what happened; students and faculty tell you why.
Chicago State University surveyed students and found a strong stated preference for in-person instruction across nearly every population. Their registration data showed the opposite; online sections filled first. Focus groups explained the gap: Students did prefer in-person, but their lives often didn’t allow for it.
Middle Tennessee State University found a related but different pattern. Students in one college said they wanted more in-person options, but the department simply wasn’t offering in-person sections. What looked like a preference was actually a constraint.
Getting a clean read on any of this depends on how you ask. Indiana University South Bend spent committee time auditing their own survey questions for bias before fielding them, work Jill Parin described as some of the most valuable time the team spent on the project: “It was fascinating to watch the work we did to make sure we were checking our own biases and that our questions weren’t actually leading people somewhere.”
Go deeper:
Get survey and focus group tools for gathering student and faculty input.
9. Closing the loop on feedback is part of the process, not an optional follow-up.
As Sally Norton of IU South Bend put it: “If you’re asking for feedback, make sure you respond to that feedback. If you don’t provide any visible response, people quit being engaged.”
Middle Tennessee’s turnaround is a useful model. The team surveyed in November and December, shared results back with faculty, deans, and student government right away, and had a proposal on the table by February, leading with a one-page summary rather than a raw data dump.
Go deeper:
Hear the full exchange with Sally Norton in Gathering Student and Faculty Input.
10. Frame the data as a starting point for collaboration.
If enrollment data reads as accusatory, the conversation can become defensive.
San José State University’s (CA) Sarah Cisneros described the reframe her team uses instead: The data is a conversation starter, not a verdict. The conversation it starts usually centers on trade-offs, since the schedule is a shared, finite resource.
As Cisneros put it, if one department doesn’t use its full share of the meeting-pattern grid, “they’re taking up all of those resources in those classrooms during that one specific time.” Treating the schedule as “a shared responsibility… also creates access for our students.”
Go deeper:
Hear the full case for this reframe.
11. Policy alone won’t change behavior; it takes transparency and accountability
Most campuses already have scheduling policies, be it a meeting-pattern grid or guardrails on off-grid exceptions. What’s often missing is a way to see whether they’re actually being followed before the schedule is finalized.
The cohort’s benchmark analysis shows the cumulative effect of that gap: patterns of compression and uneven utilization that build up, exception by exception, without anyone tracking them in aggregate.
AASCU’s broader course scheduling playbook, drawing on an earlier pilot cohort, makes the same point plainly:
“Clearly articulated policies with transparent enforcement ensure that the needs of all concerned parties—faculty, administrators, and students—are taken into account,” leading to “shared expectations and greater predictability, and more measurable outcomes from a balanced schedule.”
No one chooses the alternative on purpose; it’s the sum of many individually reasonable exceptions.
Go deeper:
Read more on codifying and enforcing scheduling policy in the “Course Scheduling Playbook.”
12. Start with what you can change this term.
The common reaction to looking honestly at a schedule for the first time is a bit of paralysis because it all seems connected to everything else.
Consider the cohort’s practical advice: Separate what you can influence this cycle from what belongs to a future one, and be explicit with stakeholders about which is which. Adding a section in a known bottleneck course, or resolving a conflict between two required courses, is often small enough to execute in a single schedule build.
As Samantha Raynor of AASCU has seen with earlier pilot institutions, teams would describe a change as just “this small thing.” Those small changes turned out to matter far more than they expected: “Each small thing has major impacts in terms of time to degree and progress towards degree.”
Go deeper:
Introduce this important work to your team.
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