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NSS open-text research brief · 2026 edition

What students said about Delivery of Teaching in NSS 2026

Delivery of Teaching appears in 25.1% of classified NSS comments in 2026.

01 · The 2026 answer

What changed from 2025?

In 2026, delivery of teaching appeared in 25.1% of classified comments (n=10,241).

The mention rate moved +0.1 percentage points from 2025. The sentiment index changed by +2.0; these are descriptive changes, not estimates of individual student satisfaction.

2025 25.0%
2026 25.1%
Share of classified NSS open-text comments that mention delivery of teaching. One comment may mention more than one topic.

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

  1. (CAH05) veterinary sciences

    n=29 · sentiment +54.9 · 27.9% mention rate

  2. (CAH19) language and area studies

    n=266 · sentiment +38.0 · 26.9% mention rate

  3. (CAH20) historical, philosophical and religious studies

    n=412 · sentiment +31.0 · 26.1% 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 1,002 22.9% +28.5
(CAH15) social sciences 971 25.5% +24.3
(CAH17) business and management 760 23.1% +23.4

Detailed subject areas

Group n Mention rate Sentiment
(CAH16-01-01) law 419 28.7% +24.1
(CAH11-01-01) computer science 394 26.4% +7.9
(CAH15-03-01) politics 319 24.8% +26.2

Age

Group n Mention rate Sentiment
Young 9,574 25.0% +22.9
Mature 655 26.0% +23.8

Disability

Group n Mention rate Sentiment
Not disabled 7,997 25.2% +23.3
Disabled 2,244 24.7% +21.6

Ethnicity

Group n Mention rate Sentiment
White 5,434 26.4% +21.5
Not UK domiciled 1,899 23.8% +24.8
Asian 1,343 23.8% +24.4

Sex

Group n Mention rate Sentiment
Female 5,982 24.5% +24.6
Male 4,225 25.9% +20.6

Mode of study

Group n Mention rate Sentiment
Full-time 9,971 25.0% +22.9
Apprenticeship 225 29.6% +23.6
Part-time 41 26.6% +28.8

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 13,223 24.6% +17.2
2024 15,733 24.6% +15.8
2025 15,012 25.0% +20.9
2026 10,241 25.1% +22.9
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 10,009 21.0% +14.7
2019 11,810 21.7% +14.7
2020 10,458 21.0% +18.5
2021 14,523 20.3% +15.8
2022 15,978 20.9% +15.4

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. 1

    Design a consistent teaching rhythm

    Set a baseline for session structure, preparation, accessible materials and follow-up, while preserving the teaching methods each discipline needs.

    Evidence: 10,241 reportable comments in 2026, 25.1% of classified comments.

  2. 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. 3

    Set the next-cycle check now

    Use short module checks to identify where delivery falls below the agreed baseline before the end of the course.

    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). “Delivery of Teaching NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/category/delivery-of-teaching/

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