What UK Students Say About Teaching Staff: NSS Feedback Analysis (25,281 Comments, 2018–2025)

Students’ comments about Teaching Staff are overwhelmingly positive across the board. The tone is consistently strong for most groups, with some notable variation by subject family and student profile that points to where attention would add the most value.

Sentiment index: +52.8 (−100 to +100).

Key findings

25,281 comments analysed across UK programmes (2018–2025); overall sentiment is positive (index +52.8)
The baseline is strong: four in five sentences about Teaching Staff are positive.
Differences are mainly profile- and subject-led. Female students report higher positivity (+56.6) than male students (+48.7).

What students are saying in this category

The baseline is strong: four in five sentences about Teaching Staff are positive. This pattern holds for both young (+53.4) and mature (+53.2) students, and for disabled (+52.4) and non‑disabled (+53.6) students.
Differences are mainly profile- and subject-led. Female students report higher positivity (+56.6) than male students (+48.7). Full‑time students score higher (+54.0) than part‑time (+49.5).
By subject family, sentiment is especially high in language and humanities areas (e.g., Languages +63.0; Historical/Philosophical/Religious +63.2) and in Medicine & Dentistry (+58.1). Lower scores cluster in technical fields (Engineering & Technology +38.3; Computing +44.6) and Business & Management (+46.4).
Ethnicity patterns are broadly positive overall but with a gap for Black students (+42.8) relative to the category average (+52.8).

Benchmarks across student segments

At‑a‑glance: overall and key segments

Segment	Comments	Positive %	Negative %	Sentiment idx
Overall	25,281	78.3	19.5	52.8
Young	19,095	78.9	19.0	53.4
Mature	5,630	77.5	20.1	53.2
Full-time	20,975	79.2	18.7	54.0
Part-time	3,544	74.7	22.6	49.5
Female	14,478	80.3	17.7	56.6
Male	10,199	76.1	21.6	48.7
Not disabled	19,615	78.7	19.2	53.6
Disabled	5,115	78.1	19.6	52.4

Subject family (CAH1) — extremes by sentiment

CAH1 subject family	Comments	Positive %	Negative %	Sentiment idx
Veterinary sciences	91	90.1	9.9	71.7
Historical, philosophical and religious studies	1,208	84.4	13.7	63.2
Language and area studies	990	83.4	14.6	63.0
Geography, earth and environmental studies	460	83.0	15.9	61.2
Medicine and dentistry	564	81.4	16.7	58.1
—
Agriculture, food and related studies	32	68.8	31.3	33.9
Engineering and technology	1,016	69.7	27.8	38.3
Physical sciences	435	74.0	24.8	44.2
Computing	1,157	72.6	24.7	44.6
Business and management	2,013	74.5	23.2	46.4

Note: Smaller subjects (e.g., Agriculture n=32; Veterinary n=91) should be interpreted with caution.

What this means in practice

Protect the baseline: keep high‑trust behaviours visible. Set and meet simple service standards (e.g., response to student queries within 2–3 working days; predictable office hours; “what to expect this week” updates). These habits sustain the already strong positive tone.
Target the gaps:
- Technical and quantitatively heavy subjects (Engineering, Computing, Business): prioritise clarity of explanations, worked exemplars, and predictable drop‑ins. Track whether students can action feedback quickly.
- Part‑time cohorts: mirror support options with out‑of‑hours contact windows and asynchronous Q&A summaries.
- Differential experiences (e.g., male and Black students): monitor sentiment by segment each term, check consistency of interactions across teaching teams, and invite quick pulse feedback after key teaching moments.
Make it measurable: use a simple dashboard to follow sentiment index by subject and cohort; review outliers monthly and close the loop with students on what changed.

How Student Voice Analytics helps you

Continuous visibility of Teaching Staff comments and sentiment over time, with drill‑downs from provider to subject family and cohort.
Like‑for‑like comparisons by CAH subject family and student demographics, plus segmentation by mode, site/campus and year of study.
Concise, anonymised summaries for programme and departmental briefings, and export‑ready tables for quality boards.

How to use this data

This page presents sector-level student feedback analysis for the Teaching Staff category (The teaching on my course), 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.