What UK Students Say About Communication About Course and Teaching: NSS Feedback Analysis (6,214 Comments, 2018–2025)

Students’ comments on course and teaching communications are predominantly negative across the board. The picture varies by mode of study, disability, ethnicity and subject area, but the overall tone points to issues with clarity, timeliness and reliability of information flows.

Key findings

6,214 comments analysed across UK programmes (2018–2025)
The category is heavily negative (−30.0), with nearly three negative comments for every positive.
Full-time students (79.2% of comments) are more negative (−32.0) than part-time students (−18.0).

What are students saying in this category?

The category is heavily negative (−30.0), with nearly three negative comments for every positive.
Full-time students (79.2% of comments) are more negative (−32.0) than part-time students (−18.0).
Disabled students report a steeper negative tone (−35.4) than those not disabled (−28.4).
Ethnicity differences are notable: White students (−32.4) are more negative than Asian (−21.1) and Black students (−13.4).
By subject group (CAH1), the strongest headwinds appear in Medicine and dentistry (−44.8) and several creative/media areas (around −35 to −39). More moderate sentiment appears in Physical sciences (−15.1) and Geography/Earth/Environmental studies (−17.3).

Low-count segments exist; interpret very small groups with caution.

Variation by segment (2018–2025)

Key segments at a glance

Segment	Group	Share %	Pos %	Neg %	Sentiment idx	n
Overall	All	100.0	24.4	72.5	−30.0	6214
Age	Young	72.2	23.9	73.0	−31.0	4485
Age	Mature	25.2	25.5	71.3	−26.6	1563
Mode	Full-time	79.2	23.1	73.7	−32.0	4920
Mode	Part-time	16.6	31.2	65.9	−18.0	1029
Disability	Disabled	19.7	19.9	76.3	−35.4	1223
Disability	Not disabled	77.7	25.5	71.6	−28.4	4827
Sex	Female	63.4	23.8	73.1	−30.6	3941
Sex	Male	33.8	25.5	71.3	−28.3	2101
Ethnicity	White	70.8	22.3	74.7	−32.4	4402
Ethnicity	Asian	9.2	30.9	65.0	−21.1	569
Ethnicity	Black	4.2	37.5	59.1	−13.4	259

Subject groups (CAH1): largest cohorts and extremes

CAH1 subject group	Share %	Pos %	Neg %	Sentiment idx	n
Unknown	20.1	20.3	76.7	−35.0	1247
(CAH02) Subjects allied to medicine	14.0	22.1	75.3	−33.8	869
(CAH15) Social sciences	7.5	28.1	68.2	−27.1	469
(CAH01) Medicine and dentistry	6.9	14.6	83.1	−44.8	431
(CAH07) Physical sciences	1.4	36.4	63.6	−15.1	88
(CAH26) Geography, earth and environmental studies	1.6	30.3	65.7	−17.3	99
(CAH24) Media, journalism and communications	0.9	19.0	79.3	−39.3	58
(CAH13) Architecture, building and planning	1.4	22.5	77.5	−35.7	89

What this means in practice

Establish a single source of truth: one channel for authoritative course information, with time-stamped updates and a brief “what changed/why/when it takes effect” note.
Publish a predictable rhythm: weekly summary, clear escalation route, and realistic response times. Minimise last‑minute changes; when unavoidable, explain promptly.
Make communications accessible: plain language, clear subject lines, structured headings, and formats compatible with assistive technologies.
Target high-need segments:
- Full-time cohorts: earlier notice of key dates and a short “no-change window” before assessments/teaching blocks.
- Apprentices and professionally intensive programmes: align calendars with external partners; maintain an explicit changes log.
- Disabled students: provide advance notice where possible and offer alternative formats by default.
Monitor subject-level outliers: where sentiment is steeply negative (e.g., Medicine and dentistry; some creative/media areas), run a monthly comms audit to check clarity, consistency and timing.

How Student Voice Analytics helps you

Track category sentiment over time and by segment (mode, age, disability, ethnicity, subject group).
Drill from provider to school/department for targeted action plans and concise briefings.
Compare like-for-like across CAH subject groups and demographics, and export tables/insights for programme teams and academic boards.

Data at a glance (2018–2025)

Volume: 6,214 comments; 100% with sentiment.
Share of all comments: ≈1.6%.
Overall mood: 24.4% Positive, 72.5% Negative, 3.1% Neutral; index −30.0.
Largest segments by share: Full-time (79.2%), Young (72.2%), White (70.8%), Not disabled (77.7%).
Notable variation: Part-time (−18.0) less negative than Full-time (−32.0); Disabled (−35.4) more negative than Not disabled (−28.4); Medicine and dentistry (−44.8) among the lowest by subject.

How to use this data

This page presents sector-level student feedback analysis for the Communication About Course and Teaching category (Organisation and management), 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.