What UK Students Say About Marking Criteria: NSS Feedback Analysis (13,329 Comments, 2018–2025)
Students discuss Marking criteria with a strongly negative tone across cohorts and subjects. The picture is consistent: perceived clarity and consistency of criteria are pain points for most groups, with only modest variation by age, study mode or subject area.
Key findings
- 13,329 comments analysed across UK programmes (2018–2025)
- The tone is highly negative overall (−44.6), indicating far more negative than positive statements about how criteria are presented and applied.
- Younger students (72.7% of this category’s comments) are more negative than mature students (sentiment −46.1 vs −41.0).
What are students saying in this category?
- The tone is highly negative overall (−44.6), indicating far more negative than positive statements about how criteria are presented and applied.
- Younger students (72.7% of this category’s comments) are more negative than mature students (sentiment −46.1 vs −41.0). Similarly, part-time students are less negative than full-time (−40.7 vs −46.0).
- By ethnicity, tone is broadly negative across all groups; it is more negative among Not UK domiciled (−47.5) and Mixed (−47.4) relative to White (−44.1).
- Across major subject areas, sentiment remains net negative. Law (−47.2) and Physical sciences (−47.1) are among the most negative; Business and management (−43.6), Psychology (−44.0) and Combined/general studies (−40.6) are slightly less negative. All high-volume areas are still well below neutral.
Segment snapshots
The table below highlights where most comments sit and how tone differs for large segments.
| Segment |
Share % |
Sentiment idx |
Positive % |
Negative % |
| Age – Young |
72.7 |
−46.1 |
7.8 |
88.5 |
| Age – Mature |
25.4 |
−41.0 |
9.1 |
87.0 |
| Mode – Full-time |
75.8 |
−46.0 |
7.8 |
88.6 |
| Mode – Part-time |
21.8 |
−40.7 |
9.1 |
86.7 |
Top subject areas by volume (share within this category):
| Subject area (CAH1) |
Share % |
n |
Sentiment idx |
Positive % |
Negative % |
| Social sciences |
11.0 |
1461 |
−45.9 |
7.4 |
88.2 |
| Subjects allied to medicine |
9.6 |
1275 |
−45.4 |
6.9 |
89.7 |
| Psychology |
8.4 |
1117 |
−44.0 |
8.1 |
89.1 |
| Business and management |
7.8 |
1035 |
−43.6 |
10.1 |
85.3 |
| Computing |
5.5 |
737 |
−44.2 |
8.1 |
89.3 |
| Law |
5.2 |
695 |
−47.2 |
7.2 |
89.1 |
| Engineering and technology |
5.0 |
665 |
−46.6 |
8.7 |
88.3 |
| Historical, philosophical and religious studies |
4.5 |
605 |
−44.1 |
9.3 |
87.3 |
Note: All figures rounded to 1 decimal for percentages and indices; counts to integers. Very small segments are not shown.
What this means in practice
Given the consistently negative tone across large cohorts and subjects, prioritise visible, consistent criteria and calibration.
- Publish annotated exemplars at key grade bands aligned to each assessment type.
- Use checklist-style rubrics with unambiguous descriptors; include weightings and common error notes.
- Release criteria early (with the brief) and hold a short Q&A or walk-through in class/online.
- Run marker calibration with a short bank of shared samples; record and publish “what we agreed” notes to students.
- Provide a brief “how your work was judged” summary with each returned grade, referencing the rubric lines ticked.
- Standardise criteria across modules where learning outcomes overlap; highlight any intentional differences up front.
- Offer a 10–15 minute feed-forward clinic before submission windows for high-volume modules.
- Track and close the loop on recurring queries about criteria (e.g., a simple FAQ linked from VLE pages).
How Student Voice Analytics helps you
- See how student sentiment on Marking criteria moves over time and by cohort, site or mode, with drill-downs from provider to school/department/programme.
- Like-for-like comparisons by CAH area and demographics (e.g., age, domicile, ethnicity, mode) to target the cohorts where tone is most negative.
- Export concise, anonymised summaries for programme teams and boards, with ready-to-use tables and year-on-year movement.
Data at a glance (2018–2025)
- Volume: ~13,329 comments on Marking criteria (≈3.5% of all comments); 100% sentiment-coded.
- Overall mood: 8.4% Positive, 87.9% Negative, 3.7% Neutral (index −44.6).
- Largest sub-groups by share: Young (72.7%), Full-time (75.8%), Female (59.9%), White (68.5%).
- All high-volume subject areas are net negative; Law and Physical sciences are among the most negative.