What Holds Higher Education Back From Proactive Student Class Demand Planning

Projecting class demand sits at the heart of effective academic scheduling, yet it remains unevenly practiced across higher education. Institutions understand that the right courses, offered in the right quantities and at the right time, directly affect student progression and resource use. Still, many scheduling decisions rely on historical patterns rather than forward-looking indicators of student need.

According to AACRAO’s Undergraduate Class and Academic Program Demand Practices survey of 331 respondents, access to the information needed to estimate class demand often remains limited. In fact, 58% of institutions report that they only “somewhat” have access to the data required for class demand forecasting. This limitation suggests that many institutions operate with an incomplete picture during schedule planning. Partial access also helps explain why forecasting practices vary widely and why institutions often rely on historical inputs rather than a more comprehensive view of student need.

Historical Registration Patterns Still Remain the Primary Input

For most institutions, estimates of class demand still begin with a look at the past. According to the AACRAO survey, 90% of respondents use registration patterns from a previous term as a source for estimating class demand. This reliance reflects the accessibility and familiarity of prior-term registration information for schedulers, particularly when other sources prove harder to access and act on.

Historical registration patterns provide a useful baseline, but they offer only a partial view of future demand. When institutions base estimates on data from a previous term, they build schedules that mirror past demand even as student progression shifts. Additionally, these schedules may not have adequately met demand when they were first established. A a result, institutions often see pressure points only once registration begins. Historical patterns offer a starting point, but they rarely tell the full story of future demand.

Barriers to Forecasting Class Demand Accurately

Accurate class demand forecasting requires more than access to the right scheduling data. It depends on the ability to analyze that information easily and with confidence. AACRAO found that 45% of institutions report insufficient analytical tools prevent more accurate use of data for class demand forecasting, underscoring the influence of tooling limitations on everyday scheduling decisions.

Without tools that take into account enrollment history, student progress, and anticipated course needs, institutions rely on fragmented and incomplete data. As a result, institutions often rely on historical sources that offer easier access, even when those inputs provide only a partial view of current student demand.

Building a More Proactive Approach to Schedule Planning

While institutions often struggle to access the right data, they are fully aware of why data-informed scheduling matters. According to the AACRAO survey, 88% of respondents say data-informed forecasting matters because it helps ensure student access to needed classes and supports timely completion. Additionally, 86% emphasize the importance of accurate resource prediction, including instructors and physical space. These responses demonstrate a shared understanding that scheduling decisions affect both student progress and institutional capacity.

To address the disconnect between student demand and class offerings, institutions can look beyond historical registration patterns and draw on forward-thinking data. For example, currently only 38% of survey respondents use educational plan data when building the course schedule. This highlights an opportunity to incorporate inputs such as degree audit data, student planning data, and program maps into class scheduling decisions. Institutions may also gain insight by examining areas where demand has not been met in the first place by analyzing overfilled sections and waitlists from previous terms.

Transitioning to this proactive, data-informed approach is a logical step, however, leveraging these data sources is often easier said than done. Many institutions don’t have the staff or expertise necessary to analyze all of these data sources. Consider how existing or new academic operations tools can assist with projecting course demand, without having to hire additional staff.