What are students actually saying about Racism Equality (NSS 2018–2025)?
Comments tagged to this category are overwhelmingly negative across cohorts and subjects, with only modest variation between major groups.
Scope: UK NSS open‑text comments tagged to Racism Equality across academic years 2018–2025.
Volume: 498 category comments within a wider dataset of 385,317 comments; 100.0% carry sentiment labels.
Overall mood: 10.0% Positive, 87.6% Negative, 2.4% Neutral (sentiment index −52.5).
What students are saying in this category
- The tone is strongly negative across the board. Full‑time students generate most of the volume (87.8%) and are very negative (−53.8). Part‑time is slightly less negative (−49.2) but still firmly adverse.
- By age, both young (−53.3) and mature (−53.0) students report similarly negative experiences.
- By sex, female (−54.4) and male (−50.1) students are comparably negative.
- Ethnicity patterns show the sharpest differences among higher‑volume groups: Black students are most negative (−62.2; n=75), while White (−49.4; n=198) and Not UK domiciled (−49.1; n=58) are slightly less negative.
- By subject area (CAH1), sentiment is consistently negative. The largest volumes come from Social Sciences (−52.8; n=65), Subjects Allied to Medicine (−54.2; n=41) and Design/Creative Arts (−53.7; n=41). Law stands out as particularly negative among mid‑sized groups (−61.2; n=22).
Trend & benchmarks
Key segments by ethnicity (higher‑volume groups)
| Ethnicity |
n |
Positive % |
Negative % |
Sentiment idx |
| White |
198 |
10.6 |
86.4 |
−49.4 |
| Asian |
78 |
9.0 |
89.7 |
−53.4 |
| Black |
75 |
4.0 |
92.0 |
−62.2 |
| Not UK domiciled |
58 |
17.2 |
82.8 |
−49.1 |
| Mixed |
35 |
11.4 |
88.6 |
−54.3 |
| Other |
21 |
9.5 |
90.5 |
−55.1 |
Top CAH subject groups by volume
| CAH subject group |
n |
Sentiment idx |
| Social sciences |
65 |
−52.8 |
| Subjects allied to medicine |
41 |
−54.2 |
| Design, and creative and performing arts |
41 |
−53.7 |
| Medicine and dentistry |
35 |
−48.2 |
| Historical, philosophical and religious studies |
35 |
−50.6 |
| Language and area studies |
30 |
−46.3 |
| Psychology |
27 |
−50.7 |
| Law |
22 |
−61.2 |
Note: Small bases (roughly n<20) are volatile; interpret with care. “Unknown/Unspecified” categories are omitted.
What this means in practice
- Make reporting easy and fast
- One clear, confidential route (online form) with named case owner.
- Acknowledge within 24–48 hours; publish target timelines for triage, investigation and closure.
- Standardise response and follow‑up
- Use a simple triage rubric (severity/recurrence/impact) to prioritise.
- Close the loop with students: brief outcome summaries and support signposting after each case.
- Preventative action in teaching spaces
- Set behavioural expectations in module handbooks and first sessions; reaffirm for groupwork and placements.
- Train staff and student leaders in active bystander responses and microaggression handling.
- Monitor equity signals
- Track incident themes and time‑to‑resolution; review quarterly with programme leads.
- Examine patterns by ethnicity, mode and site; act on clusters (e.g., specific modules/locations).
- Communicate progress
- Termly “You said, we did” focused on safety, respect and inclusion.
- Publish a short dashboard: incident volumes, resolution times, training completion, and actions closed.
How Student Voice Analytics helps you
- Turns all NSS comments into structured topics and per‑sentence sentiment so you can see movement by term and year, not just one survey point.
- Drill from provider to school/department and module themes; compare like‑for‑like across CAH subject areas and key demographics (ethnicity, age, mode, domicile).
- Share concise, anonymised summaries and export tables for programme boards, EDI committees and governing bodies.
Data at a glance (2018–2025)
- Volume: 498 comments in this category; 100.0% sentiment coverage (dataset total 385,317 comments).
- Mood: 10.0% Positive, 87.6% Negative, 2.4% Neutral; index −52.5.
- Largest segments by volume: Full‑time 87.8%; Young 75.5%; Female 64.1%; White 39.8%.
- Most negative higher‑volume ethnicity: Black (−62.2; n=75).