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

Across the NSS, comments about Feedback lean negative overall. Younger and full‑time students drive most of the negativity, while mature and part‑time cohorts are notably more positive. Tone varies by subject area: some disciplines are net positive, but several large STEM‑adjacent areas sit firmly negative.

Scope: UK NSS open‑text comments for the Feedback category across academic years 2018–2025.
Volume: 27,344 comments; 100.0% with sentiment classification.
Overall mood: 33.5% Positive, 57.3% Negative, 9.3% Neutral (sentiment index −10.2).

What are students saying in this category?

The overall balance is unfavourable (−10.2), with 57.3% of sentences classed as negative. This suggests students frequently find feedback lacking in timeliness, usefulness, or clarity.
Cohort differences are material. Mature and part‑time students register net‑positive sentiment (indices +4.4 and +6.7), pointing to practices in these modes worth emulating. Young and full‑time cohorts are markedly negative (−15.8 and −16.1).
By subject area, sentiment is most negative in medicine/dentistry (−21.6) and biological & sport sciences (−16.6), and most positive in combined/general studies (+6.1) and education & teaching (+4.0).
Disabled students are slightly more negative than non‑disabled (−11.6 vs −10.6). Non‑UK‑domiciled students are also more negative than average (−15.1).

Benchmarks by segment

Segment	Group/value	n	Pos %	Neg %	Sentiment idx
Overall	All students	27,344	33.5	57.3	−10.2
Age	Young	19,390	27.8	62.0	−15.8
Age	Mature	6,472	42.6	49.7	+4.4
Mode	Full‑time	19,655	28.1	61.9	−16.1
Mode	Part‑time	6,056	42.6	49.0	+6.7
Disability	Disabled	4,475	31.0	61.3	−11.6
Ethnicity	Not UK domiciled	2,506	25.3	61.3	−15.1

Subject areas (CAH1): where tone is strongest/weakest

Subject area (CAH1)	n	Pos %	Neg %	Sentiment idx
Combined and general studies (CAH23)	995	43.7	48.9	+6.1
Education and teaching (CAH22)	552	42.4	48.9	+4.0
Language and area studies (CAH19)	974	43.6	49.3	+2.2
Design, creative and performing arts (CAH25)	865	40.5	51.4	+2.2
Computing (CAH11)	1,467	29.2	60.9	−14.1
Engineering and technology (CAH10)	1,380	30.2	57.9	−14.5
Biological and sport sciences (CAH03)	1,181	28.0	62.9	−16.6
Veterinary sciences (CAH05)	58	27.6	60.3	−18.8
Medicine and dentistry (CAH01)	659	25.6	64.0	−21.6

Note: Very small segments (e.g., Veterinary sciences, n=58) should be treated with caution.

What this means in practice

Reset the basics: timeliness and usefulness
- Publish a clear feedback SLA by assessment type and track on‑time rates.
- Require structured “feed‑forward” (what to do next) alongside criteria‑referenced comments.
- Use concise rubrics with annotated exemplars to reduce ambiguity.
Target the largest and most negative cohorts first
- Young and full‑time cohorts show the least favourable tone; prioritise these with consistent turnaround, clearer criteria, and short “how to use your feedback” guides within modules.
Calibrate where tone is weakest by subject
- Run quick calibration sprints (shared marking of samples) in medicine/dentistry, biological & sport sciences, engineering and computing.
- Add spot checks on feedback quality (specificity, actionability, alignment to criteria).
Lift practice from where it’s working
- Capture approaches from mature and part‑time provision (e.g., staged feedback, dialogic sessions, checklists) and replicate in high‑volume full‑time modules.
Close the loop visibly
- Share brief “you said → we did” updates each term highlighting on‑time performance and changes to feedback formats.

How Student Voice Analytics helps you

Turns all NSS open‑text into trackable metrics for Feedback: sentiment over time, comment volumes, and segment differences by age, mode, disability, domicile, and subject (CAH).
Enables drill‑downs from provider to school/department/programme, plus cohort and site where available. Export concise, anonymised summaries for module teams and boards.
Provides like‑for‑like comparisons across CAH areas and demographics, so you can prioritise where tone is weakest and evidence improvement.

Data at a glance (2018–2025)

Volume: 27,344 comments; 100.0% with sentiment classification.
Overall mood: 33.5% Positive, 57.3% Negative, 9.3% Neutral (index −10.2).
Cohorts to prioritise: Young (−15.8) and Full‑time (−16.1); learn from Mature (+4.4) and Part‑time (+6.7).