What are students actually saying about Graduation (NSS 2018–2025)?
Graduation attracts a small but distinctly negative set of comments. Most feedback comes from full-time and younger students, with differences by subject area and ethnicity.
Scope: UK NSS open-text comments tagged to Graduation across academic years 2018–2025.
Volume: 408 comments (~0.1% of all 385,317 comments); 100.0% sentiment coded.
Overall mood: 31.1% Positive, 65.7% Negative, 3.2% Neutral (sentiment index −14.9).
What are students saying in this category?
- The tone is net negative overall (−14.9), driven mainly by full-time (−14.4) and younger students (−15.8), who contribute most of the volume.
- By subject area (CAH1), sentiment varies widely. Design, and creative and performing arts is notably positive (+22.1; n=26), while Law (−35.7; n=23) and Social sciences (−24.3; n=51) are strongly negative. Subjects allied to medicine sits closer to neutral (−9.4; n=40).
- Mature students are less negative than younger students (−8.3 vs −15.8). Part-time students are slightly less negative than full-time (−11.6 vs −14.4).
- Ethnicity differences are evident: White students sit at −10.9 (n=254), compared with Asian −23.8 (n=58) and Mixed −34.8 (n=17). Disabled respondents are somewhat less negative than those reporting no disability (−4.8 vs −16.1).
Segment snapshot
Selected groups with at least modest volume; “Unknown/Unspecified” omitted.
| Segment | N | Positive % | Negative % | Sentiment idx |
|---|---|---|---|---|
| Age — Young | 308 | 30.2 | 66.2 | −15.8 |
| Age — Mature | 88 | 36.4 | 61.4 | −8.3 |
| Mode — Full-time | 344 | 30.5 | 66.3 | −14.4 |
| Mode — Part-time | 51 | 39.2 | 56.9 | −11.6 |
| Disability — Not disabled | 328 | 29.9 | 67.1 | −16.1 |
| Disability — Disabled | 68 | 39.7 | 55.9 | −4.8 |
| Ethnicity — White | 254 | 34.6 | 61.8 | −10.9 |
| Ethnicity — Asian | 58 | 24.1 | 72.4 | −23.8 |
| Ethnicity — Mixed | 17 | 11.8 | 82.4 | −34.8 |
| Ethnicity — Black | 11 | 27.3 | 72.7 | −21.9 |
| Sex — Female | 281 | 31.0 | 65.1 | −13.9 |
| Sex — Male | 114 | 32.5 | 65.8 | −15.0 |
By subject area (CAH1) — largest groups
Caution: some groups are small; results can be volatile at low n.
| Subject area (CAH1) | N | Positive % | Negative % | Sentiment idx |
|---|---|---|---|---|
| Social sciences (CAH15) | 51 | 25.5 | 72.5 | −24.3 |
| Business and management (CAH17) | 49 | 28.6 | 69.4 | −18.7 |
| Subjects allied to medicine (CAH02) | 40 | 32.5 | 62.5 | −9.4 |
| Design, and creative and performing arts (CAH25) | 26 | 61.5 | 30.8 | +22.1 |
| Law (CAH16) | 23 | 13.0 | 82.6 | −35.7 |
| Medicine and dentistry (CAH01) | 21 | 23.8 | 71.4 | −21.5 |
What this means in practice
- Get the basics out early and in one place
- Publish a single source of truth for dates, venue, guest limits/tickets, gowning, photography, costs and deadlines; update with a simple “what changed” log.
- Target comms to full-time and younger cohorts (the largest and most negative groups).
- Plan for inclusive, predictable delivery
- Stress-test accessibility (seating, step-free routes, assistive support, quiet spaces) and name pronunciation capture/verification.
- Offer live stream/overflow options and clear refund policies for late changes.
- Manage demand and expectations
- Use clear ticket policies (allocation + waiting list) and transparent cut-offs; provide status updates if demand outstrips capacity.
- Local ownership where sentiment is weakest
- Assign a school-level lead in areas showing stronger negativity (e.g., Law, Social sciences) to run a brief “ceremony readiness” checklist and close gaps.
- Service recovery on the day
- Stand up an issues desk with rapid triage, on-the-spot fixes (e.g., reprints, seating moves), and post-event follow-up within a published timeframe.
How Student Voice Analytics helps you
- See Graduation feedback and sentiment over time, with drill-downs by subject area (CAH), age, mode, disability, ethnicity and sex. Segment-level views surface where tone differs and where to intervene.
- Produce concise, anonymised summaries for programme and operations teams. Benchmark like-for-like across CAH groupings and demographic segments, explore cohort/site differences, and export briefings for rapid action.