What are students actually saying about Library (NSS 2018–2025)?
Students are broadly positive about the library. Two-thirds of comments are positive, with a healthy sentiment index and clear variation by student group and subject.
Scope: UK NSS open-text comments for Library across academic years 2018–2025.
Volume: ~6,731 comments; 100% assigned a sentiment.
Overall mood: 65.0% Positive, 33.1% Negative, 1.8% Neutral (≈1.96:1 positive:negative; sentiment index +30.1).
What students are saying in this category
- Overall tone is positive (+30.1), with low neutrality (1.8%). Most comments come from full-time (80.9%) and younger students (72.8%).
- Differences by demographic are consistent: younger students (index +33.1) are more positive than mature (+24.6); full-time (+32.2) more positive than part-time (+24.1); males (+35.6) more positive than females (+28.1). Students reporting a disability are somewhat less positive (+27.0) than those not reporting a disability (+31.9).
- By ethnicity (as recorded), “Not UK domiciled” stands out as very positive (+51.1). White students are positive overall (+28.2), with stronger tones among Black (+37.3), Mixed (+35.5) and Asian (+30.6) groups.
- Subject patterns vary. Positive outliers include Computing (+47.3; n=181), Subjects allied to medicine (+43.9; n=616) and Engineering (+39.4; n=234). Lower indices appear in Language and area studies (+20.5; n=279) and Combined and general studies (+10.7; n=199). Very small groups can show extreme values.
Segment benchmarks (selected)
| Segment |
Comments |
Positive % |
Negative % |
Sentiment idx |
| Overall |
6,731 |
65.0 |
33.1 |
30.1 |
| Age — Young |
4,897 |
66.9 |
31.2 |
33.1 |
| Age — Mature |
1,618 |
60.8 |
37.5 |
24.6 |
| Mode — Full-time |
5,448 |
66.3 |
31.8 |
32.2 |
| Mode — Part-time |
1,026 |
60.6 |
37.5 |
24.1 |
| Sex — Female |
4,044 |
63.4 |
35.0 |
28.1 |
| Sex — Male |
2,466 |
68.6 |
29.0 |
35.6 |
| Disability — Not disabled |
5,263 |
66.0 |
32.2 |
31.9 |
| Disability — Disabled |
1,256 |
62.7 |
35.2 |
27.0 |
| Ethnicity — Not UK domiciled |
487 |
80.9 |
18.5 |
51.1 |
| Ethnicity — White |
4,722 |
62.9 |
35.2 |
28.2 |
| Ethnicity — Asian |
532 |
66.7 |
31.0 |
30.6 |
| Ethnicity — Black |
247 |
70.9 |
26.3 |
37.3 |
Subject patterns (CAH1) — largest groups by volume
| Subject group (CAH1) |
Comments |
Positive % |
Negative % |
Sentiment idx |
| Unknown |
1,403 |
63.5 |
34.2 |
29.5 |
| Social sciences |
774 |
64.5 |
34.5 |
28.1 |
| Subjects allied to medicine |
616 |
74.8 |
24.4 |
43.9 |
| Business and management |
538 |
67.7 |
30.5 |
33.6 |
| Historical, philosophical and religious studies |
537 |
61.3 |
36.9 |
25.4 |
| Law |
383 |
63.4 |
33.4 |
29.4 |
| Psychology |
317 |
65.9 |
31.2 |
31.2 |
| Language and area studies |
279 |
56.3 |
41.9 |
20.5 |
| Engineering and technology |
234 |
73.1 |
25.6 |
39.4 |
| Combined and general studies |
199 |
52.8 |
47.2 |
10.7 |
What this means in practice
- Close the experience gap for mature and part-time students. Prioritise access routes that work outside typical hours and reduce friction for time‑poor learners; check whether service touchpoints (help channels, inductions, updates) are equally visible and usable for these groups.
- Target subject areas with lower sentiment. Partner with schools in Language and area studies and Combined/general to review core needs (e.g., resource discoverability, availability, skills support) and set small, measurable fixes.
- Sustain and spread what works. Capture practices from higher‑scoring areas (e.g., Computing, Subjects allied to medicine, Engineering) and replicate where appropriate.
- Improve accessibility for disabled students. Validate the end‑to‑end journey (physical spaces, digital platforms, assistive technologies, staff confidence) and publicise adjustments clearly.
- Keep a simple feedback loop. Publish a short “you said, we did” by segment/subject and track the sentiment index quarterly to evidence change.
How Student Voice Analytics helps you
- Turns all NSS open-text into topic and sentiment metrics for Library, with drill‑downs by school/department (CAH), demographics, and mode/campus/site.
- Surfaces where tone diverges (e.g., mature vs young; part‑time vs full‑time; specific subject groups), with export‑ready summaries for quick briefing.
- Enables like‑for‑like comparisons across CAH codes and demographics, plus segmentation by cohort or site to prioritise actions and monitor impact over time.
Data at a glance (2018–2025)
- Volume: ~6,731 Library comments; 100% with sentiment.
- Overall mood: 65.0% Positive, 33.1% Negative, 1.8% Neutral (index +30.1).
- Composition (share of Library comments): Full-time 80.9%, Part-time 15.2%; Young 72.8%, Mature 24.0%.