Academic program and departmental reviews take many different shapes—from pure, qualitative descriptions of accomplishments and goals to detailed reports on contributions to the discipline.
But what do most have in common? They are time-consuming. They are arduous. And despite all of the effort required, they often fail to connect the dots between how the program is performing and broader institutional goals. (Did I mention they are time-consuming?)
In our study, Academic Vital Signs, EAB researchers interviewed 100+ department, college, and university leaders on how they evaluate department success. One critical principle the study revealed is that provosts should meet annually with their deans and department chairs to review data, discuss resource needs, and set annual performance goals. More frequent reviews give department leaders more visibility into how their decisions impact institutional strategy and also allows for more flexibility in strategic planning—giving leaders time to pivot and adjust to changes, rather than a stressful prioritization process every few years.
The idea sounds promising, but implementing an annual review process is a daunting prospect for most colleges and universities. Many have concerns: Wouldn't more frequent reviews just create more work and stress? What type of data should we review? How do I get department leaders on-board with this process?
EAB's Academic Performance Solutions (APS) technology can help make an annual review process easier and more efficient. And while there are numerous metrics that departments could review to evaluate performance, the most pressing institutional priorities involve capacity and costs, enrollment growth, and student outcomes. See how ready-to-use dashboards in APS can help answer common questions in each of these three areas.
Concern: Instructional capacity and costs
Are we maximizing instructional capacity without overburdening faculty and sacrificing academic rigor?
Analysis of APS benchmarking data indicates that most colleges and universities are only using around 75% of their optimal capacity, measured by student credit hour (SCH) production. Over half of the difference between the actual SCH produced and optimal capacity can be attributed to faculty not teaching full course loads.
Matching metrics: Faculty credit hours and student credit hours taught by faculty type
Use the APS platform to analyze how many courses your full-time staff teach and how many student credit hours are produced. Then compare the results to internal and external benchmarks. If faculty teach an average or above average number of hours, but the department produces below average SCH, look into analyses of fill rates and class size to find potential opportunities to address productivity.
Concern: Enrollment growth
Are we attracting students and maintaining demand?
To make smart resource decisions, academic leaders need not only to know the overall trend of enrollment, but also the source of that student demand. They can then find opportunities to meet demand through non-traditional means, such as offering summer and winter sessions. Having more granular data allows a department chair to diagnose, for example, that SCH growth in architecture and planning studies is not driven by architecture majors (as illustrated below).
Matching metrics: Trends in attempted student credit hours by term and share taught to majors
In the APS platform, analyze overall trends in attempted SCH from year to year, focusing both on total growth or decline, as well as changes in share taught to program majors. Look into off-peak enrollment during summer and winter sessions, where there is opportunity to increase numbers, reduce time to degree, and generate revenue.
Concern: Student outcomes
How can we better support students and reduce curricular barriers?
Success in critical first-year or introductory courses has a significant impact on overall student success at a college or university. Department leaders should review overall course completion rates, as well as section completion rates for these gateway courses. Looking at the section-level completion rate will illuminate possible disparities in student experience due to differences in pedagogy and grading practices across multiple instructors of the same course.
Matching metrics: Completion rate and attempted credit hour production by course
Using the APS platform, focus on courses with low completion rates and high attempted student credit hours to prioritize resource allocation where students struggle the most. Look into section-level course completion rates to understand where course redesign or instructor collaboration may be necessary to ensure similar student experiences across sections.
Once there are clear expectations for which metrics will be reviewed and how the review will impact resource decisions, department leaders should also be granted access to data in a way that is both easy to use and transparent. While the instinct may be to keep data separated by department and to allow department leaders to select their own metrics to make their case for new resources, providing a single source of data offers the necessary structure and guidance for productive conversations that are centered on institutional priorities.