NSS open-text research brief · 2026 edition
What Finance 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 453 classified comments in Finance.
Teaching Staff is the leading reportable topic in this brief. Its mention rate changed by −5.5 percentage points and its sentiment index by +0.8 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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Student Life
n=35 · sentiment +63.9 · +20.6 points vs sector · 7.7% mention rate
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Availability of Teaching Staff
n=20 · sentiment +50.1 · +7.0 points vs sector · 4.4% mention rate
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Student Support
n=70 · sentiment +49.1 · +14.6 points vs sector · 15.5% mention rate
Below-sector sentiment
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Assessment Methods
n=40 · sentiment −24.4 · −7.4 points vs sector · 8.8% mention rate
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Delivery of Teaching
n=113 · sentiment +16.3 · −6.6 points vs sector · 24.9% mention rate
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Module Choice and Variety
n=41 · sentiment +17.5 · −3.5 points vs sector · 9.1% 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 | 127 | 28.0% | +39.7 | −2.2 |
| Delivery of Teaching | 113 | 24.9% | +16.3 | −6.6 |
| Type and Breadth of Course Content | 102 | 22.5% | +27.2 | +1.3 |
| Student Support | 70 | 15.5% | +49.1 | +14.6 |
| Feedback | 67 | 14.8% | −2.7 | +9.8 |
| Organisation and Management of Course | 47 | 10.4% | +26.9 | +35.1 |
| Learning Resources | 41 | 9.1% | +42.4 | +18.9 |
| Module Choice and Variety | 41 | 9.1% | +17.5 | −3.5 |
| Assessment Methods | 40 | 8.8% | −24.4 | −7.4 |
| Student Life | 35 | 7.7% | +63.9 | +20.6 |
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 |
|---|---|
| 2023 | 526 |
| 2024 | 723 |
| 2025 | 968 |
| 2026 | 453 |
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 | 406 |
| 2019 | 400 |
| 2020 | 322 |
| 2021 | 163 |
| 2022 | 240 |
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 the purpose of assessment explicit
Map each assessment to its learning purpose, required preparation and place in the programme, then remove avoidable duplication and bunching.
Evidence: Assessment Methods has a 2026 sentiment index of −24.4 from n=40 comments.
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2
Stabilise the timetable earlier
Publish confirmed teaching and assessment patterns as early as possible, coordinate deadlines at programme level and explain unavoidable changes promptly.
Evidence: Scheduling and Timetabling has a 2026 sentiment index of −18.8 from n=24 comments.
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3
Guarantee usable feed-forward
Set turnaround standards and require feedback to identify what worked, what needs attention and what the student should do differently next time.
Evidence: Feedback has a 2026 sentiment index of −2.7 from n=67 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). “Finance student feedback: NSS open-text insights, 2026.” Reviewed by Dr Stuart Grey. Student Voice AI. https://www.studentvoice.ai/cah3/finance/