What UK Students Say About Library: NSS Feedback Analysis (6,731 Comments, 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.

Key findings

6,731 comments analysed across UK programmes (2018–2025)
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-ti...

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%.

How to use this data

This page presents sector-level student feedback analysis for the Library category (Learning resources), 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.