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NSS open-text research brief · 2026 edition

What History students said in NSS 2026

Teaching Staff is the most frequently mentioned reportable topic in 2026.

01 · The 2026 answer

What changed from 2025?

The 2026 analysis covers 645 classified comments in History.

Teaching Staff is the leading reportable topic in this brief. Its mention rate changed by +2.6 percentage points and its sentiment index by +5.0 from 2025.

Teaching Staff

2025 36.0%
2026 38.6%
Share of classified History comments mentioning this topic.

02 · Findings

Where sentiment differs from the sector

Topics within this subject are shown only when at least 20 comments support the cut.

Above-sector sentiment

  1. Availability of Teaching Staff

    n=36 · sentiment +49.9 · +6.8 points vs sector · 5.6% mention rate

  2. Library

    n=22 · sentiment +49.7 · +3.9 points vs sector · 3.4% mention rate

  3. Extra-curricular Activities

    n=23 · sentiment +47.3 · +5.2 points vs sector · 3.6% mention rate

Below-sector sentiment

  1. Strike Action

    n=26 · sentiment −63.5 · −7.0 points vs sector · 4.0% mention rate

  2. Costs and Value for Money

    n=31 · sentiment −49.8 · −3.7 points vs sector · 4.8% mention rate

  3. Contact Time

    n=43 · sentiment −42.8 · −17.3 points vs sector · 6.7% mention rate

03 · Comparisons

Topics compared with the sector

Subject and sector figures are calculated on a like-for-like basis using the same deterministic supervised learning approach.

Topic n Mention rate Sentiment Vs sector
Teaching Staff 249 38.6% +39.4 −2.5
Type and Breadth of Course Content 183 28.4% +23.9 −2.0
Delivery of Teaching 169 26.2% +26.3 +3.4
Student Support 145 22.5% +29.2 −5.3
Feedback 100 15.5% −11.6 +0.9
Module Choice and Variety 96 14.9% +13.1 −7.9
Assessment Methods 80 12.4% −19.9 −2.9
Organisation and Management of Course 74 11.5% −23.4 −15.2
Student Life 66 10.2% +19.9 −23.4
Marking Criteria 66 10.2% −37.1 +6.7

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
2023 1,266
2024 1,338
2025 903
2026 645
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
2018 2,119
2019 1,776
2020 1,648
2021 2,253
2022 2,111

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

    Prepare a clear disruption protocol

    State how learning, assessment, communication and mitigation will be handled during industrial action, with named decisions and regular updates.

    Evidence: Strike Action has a 2026 sentiment index of −63.5 from n=26 comments.

  2. 2

    Make costs and educational value visible

    Publish realistic course costs early, remove avoidable extras and show how major investments connect to the learning outcomes students experience.

    Evidence: Costs and Value for Money has a 2026 sentiment index of −49.8 from n=31 comments.

  3. 3

    Show the value of planned contact

    Explain the role of each contact point, protect essential sessions and make the relationship between taught, guided and independent learning explicit.

    Evidence: Contact Time has a 2026 sentiment index of −42.8 from n=43 comments.

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). “History student feedback: NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/cah3/history/

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