Assessment at Scale: Clarity, Relevance and Manageability

Part 6 of our series on teaching at scale explores the topic of assessment. With 500+ students, your assessment plan has to work for everyone, from students and staff to systems and schedules. In this post, colleagues Dr Cliff Li and colleagues teaching on very large modules in the University of Exeter Business School outline best practice for designing assessments that work at scale.

In extra-large modules, assessment isn’t just an academic issue, it’s a logistical one. A poorly planned assessment can overload support teams, create confusion among markers, and trigger a flood of mitigation requests. When your module enrols 500+ students, scalable, transparent assessment design is essential.

Exeter educators working on large modules have shared a range of strategies to ensure assessments remain meaningful, fair, and manageable.

1. Design for Clarity

A strong assessment starts with a clear brief. Ambiguity in expectations can lead to inconsistent submissions and uneven marking. To reduce confusion:

·       Create a detailed PowerPoint guide walking students through the assessment

·       Deliver a dedicated briefing session (and record it clearly on ELE)

·       Include model submissions or annotated examples to clarify expectations

Clarity up front significantly reduces student anxiety and post-briefing queries, and frees up valuable staff time.

2. Prioritise Relevance

Students need to see why an assessment matters. Does it build real-world skills? Does it reflect their disciplinary background or entrepreneurial ambitions? In extra-large, interdisciplinary modules, the best assessments allow flexibility while staying anchored in the module’s learning outcomes.

Some effective approaches include:

·       Group projects that mirror startup or innovation challenges

·       Reflective reports linking theory to students’ career goals

·       Problem-based scenarios drawn from varied sectors (private, public, social)

Avoid relying on core texts or generalised theory alone – this not only opens the door to AI-generated answers but can also alienate students looking for practical value.

3. Minimise Mitigation Risks

Even small percentages of mitigation requests can create major workload when scaled. Where possible:

·       Avoid time-constrained in-person tests

·       Use asynchronous submission formats

·       Offer formative milestones that guide students toward summative success

Formative assessments (e.g., short reflections or activities tied to each learning block) help students stay engaged and reduce the risk of last-minute panic.

4. Support the Marking Process

As covered in an earlier blog, consistent marking starts with consistent preparation. But assessment design also influences how straightforward marking will be.

To keep things efficient:

·       Design rubrics that are easy to interpret

·       Ensure the assessment explicitly links to module content (reducing “off-piste” submissions)

·       Offer students templates or frameworks to organise their work

This reduces ambiguity and makes it easier for markers to assess fairly, especially those who haven’t taught on the module.

5. Make Feedback Meaningful

At scale, feedback can become impersonal. But students still value it – especially when it’s targeted and actionable.

Try:

·       Allocating time for “feedback clinics” where students can discuss their comments

·       Providing common feedback summaries on typical strengths/weaknesses

·       Encouraging self-reflection activities post-assessment

Even small gestures toward personalised support can help students feel valued and invested in the learning journey.

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