students unions + (CAH10-01-02) mechanical engineering
Do UK student unions benefit mechanical engineering students?
Learn about how UK student unions impact mechanical engineering students' education, opportunities, and campus life.
What UK Students Say About Students' Unions: NSS Feedback Analysis (2,410 Comments, 2018–2025)
Students’ comments on Students' Unions are predominantly negative. Around two-thirds of the 2,410 comments carry a negative tone (63.4%), yielding a sentiment index of −19.3. Positives are in the minority (31.9%). Full-time and younger students drive most of the volume, and there are clear differences by sex and subject area.
Key findings
- 2,410 comments analysed across UK programmes (2018–2025)
- The topic skews negative across the board. Full‑time students (94.6% of comments) are notably downbeat (index −20.3).
- Tone differs by sex: males are much more negative (index −30.6; 71.2% negative) than females (−7.0; 54.9% negative).
What are students saying in this category?
- The topic skews negative across the board. Full‑time students (94.6% of comments) are notably downbeat (index −20.3).
- Tone differs by sex: males are much more negative (index −30.6; 71.2% negative) than females (−7.0; 54.9% negative).
- By ethnicity, White students are more negative (−25.0) than Not UK‑domiciled students (−6.0). Some smaller groups show more balanced tone (Mixed +5.4; Black +4.2), but bases are small.
- Subject area variation is substantial. Strongly negative in historical/philosophical/religious studies (−39.4), languages (−38.7), and physical sciences (−37.7). More balanced in psychology (+4.5) and close to neutral in architecture (+1.5), though these are smaller bases.
Trend & benchmarks (segment view)
Demographic snapshot
| Segment | Group | n | Pos % | Neg % | Neu % | Sentiment idx |
|---|---|---|---|---|---|---|
| All | — | 2410 | 31.9 | 63.4 | 4.7 | −19.3 |
| Sex | Male | 1275 | 24.0 | 71.2 | 4.8 | −30.6 |
| Sex | Female | 1085 | 40.4 | 54.9 | 4.7 | −7.0 |
| Mode | Full-time | 2281 | 31.3 | 64.0 | 4.7 | −20.3 |
| Mode | Part-time | 84 | 38.1 | 57.1 | 4.8 | −8.7 |
| Age | Young | 2152 | 31.6 | 63.4 | 4.9 | −19.8 |
| Age | Mature | 218 | 29.8 | 67.4 | 2.8 | −19.9 |
| Disability | Not disabled | 1847 | 31.5 | 64.0 | 4.4 | −20.0 |
| Disability | Disabled | 524 | 31.5 | 62.8 | 5.7 | −19.0 |
| Ethnicity | White | 1711 | 28.2 | 67.3 | 4.4 | −25.0 |
| Ethnicity | Not UK dom. | 180 | 39.4 | 56.1 | 4.4 | −6.0 |
Subject‑area variation (CAH1, top by volume)
| CAH area (CAH code) | n | Pos % | Neg % | Sentiment idx |
|---|---|---|---|---|
| Social sciences (CAH15) | 290 | 24.5 | 68.3 | −28.0 |
| Historical, philosophical & religious (CAH20) | 187 | 17.1 | 76.5 | −39.4 |
| Business & management (CAH17) | 157 | 42.7 | 53.5 | −8.3 |
| Computing (CAH11) | 112 | 35.7 | 58.0 | −12.3 |
| Subjects allied to medicine (CAH02) | 110 | 37.3 | 60.9 | −11.3 |
| Engineering & technology (CAH10) | 108 | 28.7 | 70.4 | −25.4 |
| Law (CAH16) | 101 | 39.6 | 53.5 | −13.4 |
| Psychology (CAH04) | 94 | 48.9 | 48.9 | 4.5 |
Note: Small bases can produce volatile indices; interpret rows with low n cautiously.
What this means in practice
- Focus on the most negative subject areas
- Co‑design a clear engagement plan with schools where tone is most negative: historical/philosophical/religious (−39.4), languages (−38.7), physical sciences (−37.7), social sciences (−28.0), engineering (−25.4).
- Actions: targeted course‑level forums, discipline‑specific representation briefings, and a monthly “you said, we did” specific to each school.
- Close the gender gap
- Males (−30.6; 1,275 comments) are substantially more negative than females (−7.0).
- Actions: test alternative messaging and timings, bring representation updates into large compulsory touchpoints, and track uptake/sentiment by cohort.
- Learn from relatively balanced pockets
- Psychology (+4.5) and areas nearer neutral (e.g., business, allied to medicine, law) offer practices to lift and shift.
- Actions: identify the events/services and rep structures that resonate in these groups and replicate in lower‑scoring schools.
- Make full‑time engagement easier to see and use
- Full‑time students supply 94.6% of comments and remain negative (−20.3).
- Actions: publish a simple monthly outcomes digest, set response/service SLAs for common requests, and keep a single, well‑advertised point of contact per school.
How Student Voice Analytics helps you
- Track topic tone over time and drill from provider to school/department, campus/site and cohort.
- Like‑for‑like comparisons across CAH codes and by demographics (mode, domicile, ethnicity, commuter status).
- Rapid, anonymised summaries you can share with SU officers and programme teams.
- Export tables and charts for Boards and Student Experience Committees.
FAQs
-
How is the “sentiment index” calculated?
We score per‑sentence sentiment (−100 to +100), then average within each category/segment. -
How are comments assigned to topics?
Each comment is assigned one primary topic (here: Students' Unions). “Share” is that topic’s proportion of all comments; this report focuses only on this topic.
Data at a glance (2018–2025)
- Volume: 2,410 comments; 100% with sentiment.
- Overall mood: 31.9% Positive, 63.4% Negative, 4.7% Neutral (index −19.3; ≈0.50:1 positive:negative).
- Composition highlights: 94.6% full‑time; 89.3% young; 52.9% male, 45.0% female (comment shares).
- Strongest negative subject areas: historical/philosophical/religious (−39.4), languages (−38.7), physical sciences (−37.7).
- More balanced areas: psychology (+4.5), architecture (+1.5), education (+7.8) — note small bases for some.
How to use this data
This page presents sector-level student feedback analysis for the Students' Unions category (Student voice), 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.
Common subject areas linked to this theme (on our blog)
Most-read posts in this category
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.
Cite this page
Student Voice AI (2025). "Students' Unions: NSS student feedback analysis (2018–2025)." Student Voice AI. https://www.studentvoice.ai/category/students-unions/
Subject specific insights on "students unions"