Institution-wide transformation almost never starts institution-wide. Programmes that reach scale follow a deliberate Diagnose, Design, Pilot, Measure and Scale sequence rather than attempting a big-bang rollout. The reason is quality: high-impact practices only deliver their benefits when they are done well (Kuh, 2008), and doing them well takes practice.
1. Diagnose
Before changing anything, map where students disengage, where employer feedback is weakest, and which faculty are already experimenting with active methods. This becomes the evidence base for the pilot proposal.
2. Design
Design a single pilot with a focused cohort, for example one course for one semester with 40–60 students, with success measures agreed in advance: engagement, assessed capability, and employer and faculty feedback.
3. Pilot
- Run for one full term with dedicated faculty support
- Collect evidence throughout, not only in a final survey
- Record difficulties honestly; they become the rollout playbook
4. Measure
Review the results against the agreed measures and decide whether to scale, adapt or stop.
5. Scale
Scaling succeeds when new faculty inherit the pilot playbook, not only the enthusiasm behind it: documented rubrics, onboarding materials and a support structure that does not depend on the original champion. Research on faculty change consistently identifies lack of training, time and incentives as the main barriers (Brownell & Tanner, 2012), so the scale-up plan should budget for all three.
Choosing the right pilot course
The first pilot should be designed to succeed and to teach the institution something. Good candidates share four features: a willing and respected faculty lead, a course with a known problem such as high failure rates or weak employer feedback, a cohort size that allows close support, and a department head who will champion the results. Avoid courses that are about to be restructured or that depend on a single external examiner’s approval cycle.
What to measure, and when
- Before the pilot: baseline results from the previous cohort, attendance and pass rates, and a short student and faculty survey.
- During the pilot: engagement data, rubric scores on key assignments, short mid-semester student feedback and faculty reflections.
- After the pilot: assessed capability against programme outcomes, comparison with the baseline, partner feedback if industry was involved, and the cost in faculty time.
Mixing quantitative results with student and faculty voice gives the review panel a rounded picture, and the same evidence can feed directly into quality assurance and accreditation reports.
From one programme to many
Successful scale-up usually moves in waves rather than all at once: two or three programmes in the second year, each with a faculty lead mentored by someone from the original pilot, then wider rollout once the support model is proven. Henderson, Beach and Finkelstein (2011) found that change strategies lasting at least a semester and combining support for individuals with changes to policy and environment were the ones most likely to work. A scale plan should budget for both.
Frequently asked questions
How big should a teaching innovation pilot be?
Large enough to produce meaningful evidence and small enough to support closely. One course for one semester with 40-60 students is a common and practical size for a first pilot.
How do you measure the success of a learning pilot?
Agree the measures in advance: typically engagement, assessed capability against learning outcomes, pass and progression rates compared with a baseline, and student, faculty and employer feedback.
Why do university pilots often fail to scale?
Because they depend on one champion, the materials and lessons are not documented, and new faculty are not given the training, time and incentives that the original team had. Planning for scale from the start avoids this.
References
- Kuh, G. D. (2008). High-impact educational practices: What they are, who has access to them, and why they matter. Washington, DC: Association of American Colleges and Universities.
- Brownell, S. E., & Tanner, K. D. (2012). Barriers to faculty pedagogical change: Lack of training, time, incentives, and tensions with professional identity? CBE Life Sciences Education, 11(4), 339-346. doi:10.1187/cbe.12-09-0163
- Henderson, C., Beach, A., & Finkelstein, N. (2011). Facilitating change in undergraduate STEM instructional practices: An analytic review of the literature. Journal of Research in Science Teaching, 48(8), 952-984.