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NSS open-text research brief · 2026 edition

What students said about Module Choice and Variety in NSS 2026

Module Choice and Variety appears in 7.5% of classified NSS comments in 2026.

01 · The 2026 answer

What changed from 2025?

In 2026, module choice and variety appeared in 7.5% of classified comments (n=3,055).

The mention rate moved +0.7 percentage points from 2025. The sentiment index changed by +2.4; these are descriptive changes, not estimates of individual student satisfaction.

2025 6.8%
2026 7.5%
Share of classified NSS open-text comments that mention module choice and variety. 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. (CAH04) psychology

    n=112 · sentiment +39.6 · 6.7% mention rate

  2. (CAH13) architecture, building and planning

    n=24 · sentiment +33.9 · 3.6% mention rate

  3. (CAH01) medicine and dentistry

    n=30 · sentiment +31.8 · 1.5% 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
(CAH15) social sciences 351 9.2% +25.0
(CAH20) historical, philosophical and religious studies 232 14.7% +25.6
(CAH17) business and management 181 5.5% +15.8

Detailed subject areas

Group n Mention rate Sentiment
(CAH15-03-01) politics 138 10.7% +25.6
(CAH15-02-01) economics 123 10.0% +22.8
(CAH11-01-01) computer science 120 8.0% +21.8

Age

Group n Mention rate Sentiment
Young 2,970 7.8% +21.3
Mature 82 3.3% +8.8

Disability

Group n Mention rate Sentiment
Not disabled 2,319 7.3% +21.4
Disabled 736 8.1% +19.7

Ethnicity

Group n Mention rate Sentiment
White 1,768 8.6% +21.7
Not UK domiciled 580 7.3% +17.8
Asian 321 5.7% +19.9

Sex

Group n Mention rate Sentiment
Female 1,866 7.7% +24.2
Male 1,171 7.2% +15.6

Mode of study

Group n Mention rate Sentiment
Full-time 3,015 7.6% +21.1
Apprenticeship 31 4.1% +8.6

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 3,542 6.6% +21.7
2024 4,539 7.1% +19.2
2025 4,099 6.8% +18.6
2026 3,055 7.5% +21.0
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 3,860 8.1% +19.6
2019 4,893 9.0% +20.6
2020 4,198 8.4% +20.6
2021 4,883 6.8% +22.1
2022 5,058 6.6% +22.9

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. 1

    Make the option pathway navigable

    Publish a stable option map with prerequisites, likely timetable constraints, capacity rules and how each choice supports progression.

    Evidence: 3,055 reportable comments in 2026, 7.5% 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

    Track unavailable choices and timetable clashes as well as satisfaction with the breadth of the module diet.

    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). “Module Choice and Variety NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/category/module-choice-and-variety/

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