What are students actually saying about Study Space (NSS 2018–2025)?

Comments on study spaces surface a modest but clear signal: tone is slightly negative overall, with sharper dissatisfaction among mature students and some subject areas, while computing and creative disciplines report notably better experiences. This looks like a capacity, zoning and visibility issue more than a universal quality problem.

Scope: UK NSS open-text comments for the Study Space category across academic years 2018–2025.
Volume: 1,049 comments (≈0.3% of 385,317 total); 100% assigned with sentiment.
Overall mood: 40.4% Positive, 57.1% Negative, 2.5% Neutral (sentiment index −3.6; positive:negative ≈ 0.7:1).

What are students saying in this category?

  • Overall tone leans negative (−3.6), with far more negative than positive mentions (57.1% vs 40.4%). Most comments come from full-time, younger students.
  • Mature students are distinctly less satisfied (−16.3) than younger peers (−2.5), a 13.8-point gap. Part-time students also skew negative (−6.2), albeit on very small volumes.
  • Disabled students are slightly more positive (+1.2) than those not disabled (−4.6). By ethnicity, White students align with the overall pattern (−5.8). International (not UK domiciled) students are modestly positive (+2.9). Mixed (+17.2) and Black (+21.2) groups are positive but small in number; the “Other” group is notably negative (−22.9).
  • Subject-area variation is pronounced. Social sciences are negative (−21.7), as are combined/general studies (−27.2, small) and media/journalism (−44.4, very small). Computing (+28.8), design/creative (+27.0) and architecture (+8.2) are positive outliers. A large “unknown subject” block (24.5% of comments) sits close to the overall tone (−4.3).

Segment snapshot — key demographics

Segment Group Comments Sentiment idx Positive % Negative %
Age Young 964 -2.5 40.9 56.5
Age Mature 75 -16.3 36.0 64.0
Disability Disabled 197 1.2 43.1 55.3
Disability Not disabled 843 -4.6 39.9 57.4
Mode Full-time 1,021 -3.3 40.6 56.8
Mode Part-time 14 -6.2 35.7 64.3
Ethnicity White 651 -5.8 39.0 58.4
Ethnicity Not UK domiciled 135 2.9 43.7 54.1

Small groups (e.g., Mixed n=33, Black n=20, Apprenticeship n=1) show more volatile indices; interpret with care.

Subject area snapshot (top by volume)

Subject area (CAH1) Comments Share % Sentiment idx
Unknown 257 24.5 -4.3
Social sciences (CAH15) 130 12.4 -21.7
Business and management (CAH17) 94 9.0 2.2
Historical, philosophical and religious studies (CAH20) 56 5.3 -10.2
Subjects allied to medicine (CAH02) 52 5.0 1.8
Engineering and technology (CAH10) 49 4.7 -7.1
Computing (CAH11) 44 4.2 28.8
Psychology (CAH04) 40 3.8 7.9
Mathematical sciences (CAH09) 40 3.8 2.1
Law (CAH16) 40 3.8 -10.6

Note: Several smaller subject areas are strongly positive (e.g., design/creative +27.0) or negative (e.g., combined/general −27.2; media/journalism −44.4) on low volumes.

What this means in practice

  • Manage capacity and noise at peak times
    • Use live occupancy displays and seat/room booking with fair-use rules (auto-release no-shows; caps on block booking).
    • Balance zones: clearly signposted silent study vs collaboration areas; mitigate bleed-through with soft furnishings and partitions.
  • Target the most negative cohorts and locations
    • Prioritise mature-student needs (quiet, bookable desks at predictable times; reliable availability during daytime hours).
    • Audit social sciences buildings for high-demand periods; reallocate underused rooms nearby as overflow study space.
  • Replicate what works in positive pockets
    • Learn from computing/creative spaces: flexible layouts, plentiful power, dependable Wi‑Fi, and frictionless booking.
  • Get the basics right, visibly
    • Power-to-seat coverage, lighting, temperature and acoustics; rapid fix SLAs for broken sockets, chairs and printers.
    • Publish simple “when and where to find a seat” guides during assessment periods.

How Student Voice Analytics helps you

  • Track this topic’s sentiment over time and drill from institution to school/department, campus/site and cohort.
  • Like-for-like comparisons across CAH subject areas and demographics (age, domicile, mode, disability), with concise summaries you can share with estates, library and programme teams.
  • Segment by provider/site and cohort to locate hotspots quickly; export visuals/tables for rapid briefing and action logs.

How to use this category hub

This page groups Student Voice blog case studies where students talk about Study Space (theme: Learning resources). Use it to find examples, then connect them to evidence you can act on.

  • Scan the most-read posts for patterns and language students use.
  • Use the hub links to move from a theme to programmes/disciplines.
  • Turn themes into evidence via Student Voice Analytics (NSS, PTES, PRES, UKES, module evaluations).

Recommended next steps

  1. Quantify: how often does this theme appear (and where)?
  2. Segment: by discipline (CAH/HECoS), level, mode, and cohort where appropriate.
  3. Benchmark: compare like-for-like to avoid cohort-mix artefacts.
  4. Act: define 1–3 changes, then track whether the theme shifts next cycle.

Subject specific insights on "study space"

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