NSS open-text research brief · 2026 edition
What students said about Teaching Staff in NSS 2026
Teaching Staff appears in 31.4% of classified NSS comments in 2026.
01 · The 2026 answer
What changed from 2025?
In 2026, teaching staff appeared in 31.4% of classified comments (n=12,828).
The mention rate moved −1.4 percentage points from 2025. The sentiment index changed by +3.4; these are descriptive changes, not estimates of individual student satisfaction.
02 · Findings
Strengths and pressure points
Subject-area differences within this topic are shown only when at least 20 comments support the cut.
Relative strengths
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(CAH05) veterinary sciences
n=34 · sentiment +68.0 · 32.7% mention rate
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(CAH22) education and teaching
n=174 · sentiment +53.1 · 37.7% mention rate
-
(CAH23) combined and general studies
n=65 · sentiment +51.5 · 27.9% mention rate
Pressure points
No negative current-year cut meets the reporting threshold.
03 · Comparisons
Where the 2026 pattern differs
Leading reportable cuts are grouped by dimension and ordered by 2026 comment volume. Each percentage is calculated within the relevant comparison group.
Broad subject areas
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| (CAH02) subjects allied to medicine | 1,268 | 28.9% | +49.2 |
| (CAH15) social sciences | 1,214 | 31.9% | +43.9 |
| (CAH17) business and management | 1,040 | 31.5% | +44.7 |
Detailed subject areas
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| (CAH16-01-01) law | 467 | 31.9% | +42.4 |
| (CAH15-03-01) politics | 442 | 34.4% | +39.1 |
| (CAH11-01-01) computer science | 439 | 29.4% | +22.6 |
Age
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| Young | 11,893 | 31.1% | +41.7 |
| Mature | 921 | 36.6% | +44.6 |
Disability
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| Not disabled | 9,666 | 30.4% | +43.0 |
| Disabled | 3,162 | 34.8% | +38.6 |
Ethnicity
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| White | 6,864 | 33.4% | +40.4 |
| Not UK domiciled | 2,376 | 29.8% | +44.6 |
| Asian | 1,646 | 29.2% | +45.5 |
Sex
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| Female | 7,959 | 32.6% | +44.0 |
| Male | 4,812 | 29.5% | +38.5 |
Mode of study
| Group | n | Mention rate | Sentiment |
|---|---|---|---|
| Full-time | 12,497 | 31.3% | +41.8 |
| Apprenticeship | 269 | 35.3% | +47.6 |
| Part-time | 57 | 37.0% | +33.5 |
04 · Time series
Current questionnaire period, 2023–2026
The 2023 NSS questionnaire redesign creates a comparability break. We show earlier years separately as context rather than drawing a trend through 2022–2023.
| Year | Comments | Mention rate | Sentiment index |
|---|---|---|---|
| 2023 | 17,586 | 32.8% | +35.0 |
| 2024 | 19,998 | 31.3% | +35.0 |
| 2025 | 19,693 | 32.8% | +38.5 |
| 2026 | 12,828 | 31.4% | +41.9 |
Show historical context, 2018–2022
All years were analysed with the same deterministic supervised learning approach, but the survey instrument differs from the current questionnaire.
| Year | Comments | Mention rate | Sentiment index |
|---|---|---|---|
| 2018 | 14,482 | 30.4% | +35.9 |
| 2019 | 16,736 | 30.8% | +36.8 |
| 2020 | 15,481 | 31.1% | +38.6 |
| 2021 | 22,005 | 30.8% | +38.5 |
| 2022 | 23,300 | 30.4% | +36.9 |
05 · Action
Three evidence-linked actions
Use the findings to choose a local test, then check the same topic and cohort again rather than treating a sector pattern as a diagnosis of one provider.
-
1
Spread effective teaching practice
Use peer review and programme-level discussion to share approaches that students value while addressing avoidable inconsistency between modules.
Evidence: 12,828 reportable comments in 2026, 31.4% of classified comments.
-
2
Start with the clearest variation
Use a local cohort cut with enough responses to identify where the process is least consistent.
Evidence rule: no displayed cohort or subject cut has fewer than 20 comments.
-
3
Set the next-cycle check now
Review topic sentiment by module or team and check whether gaps narrow after targeted development.
Compare 2027 with 2026 on a like-for-like basis before describing movement.
06 · Method and limits
How to read this evidence
How topics are identified
Deterministic supervised learning models identify topics in each sentence. A comment counts once in every topic it mentions; mention rate is the share of comments included in the analysis for the same population, so topic rates do not sum to 100%.
Sentiment index
The index summarises the balance of positive and negative language from −100 to +100. Scores are averaged within each comment first, so longer comments do not carry more weight.
When results are shown
Pages require at least 100 comments and three reportable topics or subject cuts. Displayed cuts require n≥20; 2026-versus-2025 claims require n≥30 in both years.
Scope
This is authorised aggregate analysis of OfS NSS national undergraduate open-text comments. In 2026, 40,822 of 43,870 source comments were classified (93.1%); mention-rate denominators exclude unclassified comments.
07 · Reuse
Cite this page
Student Voice research team (2026). “Teaching Staff NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/category/teaching-staff/