NSS open-text research brief · 2026 edition
What English Literature 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 429 classified comments in English Literature.
Teaching Staff is the leading reportable topic in this brief. Its mention rate changed by −6.8 percentage points and its sentiment index by +10.4 from 2025.
Teaching Staff
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
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Teaching Staff
n=162 · sentiment +46.0 · +4.1 points vs sector · 37.8% mention rate
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Delivery of Teaching
n=129 · sentiment +41.9 · +19.0 points vs sector · 30.1% mention rate
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Student Support
n=107 · sentiment +36.7 · +2.2 points vs sector · 24.9% mention rate
Below-sector sentiment
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Costs and Value for Money
n=22 · sentiment −69.9 · −23.8 points vs sector · 5.1% mention rate
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Contact Time
n=35 · sentiment −48.0 · −22.5 points vs sector · 8.2% mention rate
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Marking Criteria
n=30 · sentiment −44.2 · −0.4 points vs sector · 7.0% 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 | 162 | 37.8% | +46.0 | +4.1 |
| Delivery of Teaching | 129 | 30.1% | +41.9 | +19.0 |
| Type and Breadth of Course Content | 121 | 28.2% | +22.9 | −3.0 |
| Student Support | 107 | 24.9% | +36.7 | +2.2 |
| Feedback | 55 | 12.8% | −7.3 | +5.2 |
| Module Choice and Variety | 53 | 12.4% | +20.6 | −0.4 |
| Organisation and Management of Course | 52 | 12.1% | −34.1 | −25.9 |
| Assessment Methods | 38 | 8.9% | −18.3 | −1.3 |
| Contact Time | 35 | 8.2% | −48.0 | −22.5 |
| Student Life | 34 | 7.9% | +40.2 | −3.1 |
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 | 563 |
| 2024 | 800 |
| 2025 | 578 |
| 2026 | 429 |
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 | 210 |
| 2019 | 800 |
| 2020 | 719 |
| 2021 | 667 |
| 2022 | 701 |
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.
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1
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 −69.9 from n=22 comments.
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2
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 −48.0 from n=35 comments.
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3
Turn criteria into shared judgements
Use plain-language criteria, annotated work at different standards and marker calibration before high-volume assessment begins.
Evidence: Marking Criteria has a 2026 sentiment index of −44.2 from n=30 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). “English Literature student feedback: NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/cah3/literature-in-english/