Average Grades by Module UK: What the Data Shows

The average grade for a specific university module is information your institution holds. It's also information they don't publish.

Ask your department what the average mark was for a module last year, and you'll likely be told it's not available, or that only aggregate degree classification data is disclosed. That's the sanitised answer. What exists behind it — cohort-level mark distributions, First rates, fail rates, median scores — is detailed, precise, and technically public under the Freedom of Information Act.

We know because we've filed hundreds of FOI requests to find out.

What "average grade" means in practice

When we talk about average grades by module, we mean the mean mark across all students who sat a module in a given academic year. That figure tells you where the centre of the distribution sits.

But the mean alone is only part of the picture. A module with a mean of 62% could have a tight distribution (most students between 55-70%) or a wide one (students spread from 35% to 85%). Those two scenarios look identical in the mean but have completely different implications for your choices.

That's why grade distribution data — not just averages — is what genuinely matters. You want to know:

• What proportion of students got a First (typically 70% or above)?
• Where does the 2:1 boundary cluster?
• Are marks compressed into a narrow band, or spread across the full range?
• Is there a long tail of low marks suggesting a difficult assessment?

These questions can't be answered from a published average. They require the full distribution — which is what FOI data provides.

What FOI data shows about module-level marks

The pattern that emerges across the FOI data GradeHack holds is more varied than most students expect.

Average marks vary significantly by subject. Some subject areas consistently produce higher average marks than others. This isn't purely because students in one subject are "better" — it reflects differences in marking conventions, assessment culture, and grade norms that have built up over decades. An average mark of 65% in engineering is often the product of very different marking standards than a 65% in creative writing. University-level benchmarking processes are supposed to address this, but they operate at degree programme level, not module level.

Assessment format is the strongest predictor of distribution shape. Modules assessed primarily through coursework and project work show wider distributions and higher average Firsts. Modules assessed through unseen examinations show tighter distributions with more students clustering around the 2:1 boundary. This pattern holds across subjects and institutions, though the magnitude varies.

Cohort size matters. Modules with fewer than 30 students can show volatile distributions from year to year — one cohort's particularly strong year can shift the First rate by 15 percentage points. Larger cohorts of 80+ students show more stable distributions. When you're looking at grade data, always check the cohort size context.

Year level affects marks in both directions. Final-year modules don't automatically produce better marks just because students are more experienced. Some final-year modules, particularly those with high conceptual difficulty or complex dissertations, produce average marks that are lower than their second-year equivalents. The assumption that "I'll do better in third year because I've got more experience" doesn't always hold.

Why this data is not routinely published

UK universities are not legally required to publish module-level grade distributions. The Higher Education Statistics Agency (HESA) collects and publishes degree classification data — the percentage of students achieving each class nationally and by institution — but this data is aggregated at programme level, not module level.

The argument universities sometimes make is that module-level data could be "misleading" without context — that a 40% First rate in one module might reflect assessment design rather than module quality, and publishing raw numbers without explanation could drive students towards grade-optimising rather than learning-optimising choices.

This is a legitimate concern. It's also somewhat self-serving for institutions that prefer opaque marking to remain that way.

The practical result is that individual students lack the information to make module choices based on anything other than reputation and word of mouth. GradeHack's position is simple: students deserve the data, provided it comes with appropriate context. That's what we're building.

How GradeHack sources this data

GradeHack's grade distribution dataset is sourced through formal Freedom of Information requests filed under the Freedom of Information Act 2000. FOI requests to UK public universities must be answered within 20 working days. Universities can refuse requests on cost or data protection grounds, but most requests for aggregated, anonymised grade statistics are answered.

The WhatDoTheyKnow platform maintains a public archive of FOI requests and responses. You can browse university responses there directly — the data is public, though navigating and comparing it manually is time-consuming. GradeHack normalises and makes this data searchable by module, institution, and year.

For more on how the FOI process works and what it reveals, see what FOI data reveals about UK university marking.

Using grade data without misreading it

A few caveats are worth understanding before you interpret module grade data.

Historical distributions are not predictions. A module that produced a high First rate in 2019 might look different in 2024 if the assessment changed, the cohort profile shifted, or a new module leader took over. Use historical data as an indicator, not a guarantee.

Never cite raw percentages without a source. The privacy rules GradeHack applies — and that we'd encourage anyone using FOI data to follow — mean we never publish exact percentages for modules with fewer than 10 students in a cohort. Below that threshold, individual students could potentially be identified. For larger cohorts, banded signals (above-average First rate, below-average fail rate) are the appropriate unit for public discussion. Exact numbers should be viewed in context, not used as a selling point for a module to friends.

Compare within institutions, not across them. A 65% mark at one university is not the same as a 65% mark at another. Marking conventions vary. Grade distributions at one institution's engineering department should be compared to other modules at that same institution — not to engineering modules at a completely different university with different grade norms.

What students should do with this information

If you're selecting optional modules — for Year 2, Year 3, or a master's programme — the most useful action you can take is to get hold of module-level grade data for your specific institution.

Start with what's publicly available: WhatDoTheyKnow contains many university FOI responses that include historical grade distributions. Some university student unions have also compiled grade data for popular optional modules, particularly at larger institutions.

For a searchable, normalised view of FOI grade data across multiple UK universities, get access to GradeHack. The data covers module-level distributions by year, with cohort size context and banded signals that make comparison meaningful rather than misleading.

Module selection is one of the few things in your degree you can genuinely control. Use the data.

See also: how to choose university modules and does module choice affect your degree class.