What are students actually saying about Teaching Staff (NSS 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.
Scope: UK NSS open-text comments for the Teaching Staff category, academic years 2018–2025.
Volume: 25,281 comments; 100.0% with sentiment.
Overall mood: 78.3% Positive, 19.5% Negative, 2.1% Neutral (positive:negative ≈ 4.0:1).
Sentiment index: +52.8 (−100 to +100).
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.