What are students actually saying about Type of students (NSS 2018–2025)?
Comments about the mix and dynamics of students are moderately positive overall, with clear differences by disability, ethnicity, mode, and subject area.
Scope: UK NSS open-text comments for Type of students across academic years 2018–2025.
Volume: 473 comments; 100.0% assigned sentiment.
Overall mood: 52.2% Positive, 44.4% Negative, 3.4% Neutral (sentiment index +10.5).
What students are saying in this category
- Most comments come from young (64.5%) and full-time (84.8%) students. Tone is positive for both, with young at +12.6 and full-time at +6.4.
- A notable gap exists by disability status: not disabled +15.7 vs disabled −4.3 (25% of comments), indicating a consistent experience penalty for disabled students.
- Part-time students are markedly more positive (+39.9) than full-time (+6.4).
- Ethnicity shows divergence: Not UK domiciled (+36.0) and Asian (+22.8) groups are positive, while Black (−11.7) and Mixed (−34.2) are negative. White students sit near neutral (+5.4). Some groups have small bases; interpret with care.
- By subject area (CAH1), Business & Management is strongly positive (+43.8; n=47), while Historical/Philosophical/Religious Studies is notably negative (−31.0; n=40). Law (−12.9; n=24) also skews negative.
Segment breakdown (2018–2025)
Key demographic splits
| Segment |
Share % |
n |
Pos % |
Neg % |
Sentiment idx |
| Age — Young |
64.5 |
305 |
54.1 |
43.0 |
12.6 |
| Age — Mature |
31.9 |
151 |
49.0 |
47.0 |
6.5 |
| Disability — Not disabled |
71.2 |
337 |
56.7 |
40.1 |
15.7 |
| Disability — Disabled |
25.4 |
120 |
40.0 |
56.7 |
-4.3 |
| Mode — Full-time |
84.8 |
401 |
49.4 |
47.4 |
6.4 |
| Mode — Part-time |
11.6 |
55 |
72.7 |
23.6 |
39.9 |
| Sex — Female |
58.1 |
275 |
46.9 |
50.9 |
2.8 |
| Sex — Male |
38.5 |
182 |
60.4 |
34.6 |
22.1 |
Ethnicity highlights (n, sentiment index): Not UK domiciled (70, +36.0), Asian (63, +22.8), White (243, +5.4), Black (33, −11.7), Mixed (11, −34.2). Treat very small groups with caution.
Top subject areas (CAH1) by volume
| Subject area (CAH1) |
Share % |
n |
Pos % |
Neg % |
Sentiment idx |
| Unknown |
16.3 |
77 |
53.2 |
42.9 |
10.0 |
| Social sciences (CAH15) |
15.0 |
71 |
49.3 |
46.5 |
10.1 |
| Subjects allied to medicine (CAH02) |
10.4 |
49 |
55.1 |
42.9 |
9.6 |
| Business & management (CAH17) |
9.9 |
47 |
76.6 |
19.1 |
43.8 |
| Historical/philosophical/religious studies (CAH20) |
8.5 |
40 |
20.0 |
80.0 |
-31.0 |
| Law (CAH16) |
5.1 |
24 |
41.7 |
50.0 |
-12.9 |
| Psychology (CAH04) |
4.2 |
20 |
55.0 |
45.0 |
15.4 |
| Engineering & technology (CAH10) |
3.4 |
16 |
62.5 |
37.5 |
26.7 |
Note: Values for groups with n<20 can be volatile.
What this means in practice
- Close the disability gap (−4.3): audit friction points in group work and class norms; ensure proactive, timely adjustments; publish a short accessibility checklist for modules; track resolution times.
- Protect part-time parity (+39.9 vs +6.4): guarantee asynchronous access to key activities; align deadlines and support hours with working patterns; provide concise weekly summaries.
- Address ethnicity disparities (e.g., Black −11.7, Mixed −34.2): set clear behaviour norms for group work; provide rapid, confidential routes to report issues; monitor outcomes by demographic without singling out individuals.
- Check programme hot/cold spots: share exemplars from Business & Management (+43.8) with areas struggling (HPRS −31.0; Law −12.9); review cohort-mix messaging, induction, and group allocation practices locally.
- Gender and age: female sentiment is near-neutral (+2.8) vs male (+22.1); mature students are less positive (+6.5). Use targeted check-ins during team projects and ensure equitable voice in seminars.
How Student Voice Analytics helps you
- Shows topic and sentiment over time for this category and drills from institution to department/programme.
- Like-for-like comparisons across CAH codes and by demographics (e.g., age, domicile, mode, campus/site).
- Quick exports with anonymised summaries for programme teams and governance.
- Segment filters (site/provider, cohort, year) to isolate where gaps are widest and evidence closure.
Data at a glance (2018–2025)
- Volume: 473 comments; 100.0% sentiment coverage.
- Overall mood: 52.2% Positive, 44.4% Negative, 3.4% Neutral (index +10.5).
- Largest gaps: Disabled (−4.3) vs Not disabled (+15.7); Full-time (+6.4) vs Part-time (+39.9); Female (+2.8) vs Male (+22.1); Black (−11.7) and Mixed (−34.2) vs Asian (+22.8) and Not UK domiciled (+36.0).
How to use this data
This page presents sector-level student feedback analysis for the
Type of Students category (Others),
with demographic and subject-area benchmarks you can reference directly in institutional documents.
Use this for
- Annual Programme Review (APR) — reference the segment benchmarks to contextualise your programme's feedback patterns against the sector.
- TEF and quality enhancement — cite the demographic breakdowns and subject-area sentiment as evidence of awareness of differential student experience.
- Equality, diversity and inclusion (EDI) — use the ethnicity, disability and age segment data to evidence where feedback experience differs by student group.
- Staff-Student Liaison Committees (SSLCs) — share the key findings and subject-area table as discussion starters with student representatives.
- Action planning — use the "What this means in practice" recommendations as a starting point for targeted interventions.
Recommended next steps
- Quantify: how often does this theme appear (and where)?
- Segment: by discipline (CAH/HECoS), level, mode, and cohort where appropriate.
- Benchmark: compare like-for-like to avoid cohort-mix artefacts.
- Act: define 1–3 changes, then track whether the theme shifts next cycle.