What UK Students Say About Workload: NSS Feedback Analysis (6,847 Comments, 2018–2025)
Workload attracts a sustained, strongly negative response across the dataset. The tone is consistently unfavourable across all segments, with particularly negative sentiment among full-time and younger students.
- Scope: UK NSS open-text comments classified to Workload across academic years 2018–2025.
- Volume: 6,847 comments (~1.8% of all 385,317 comments); 100% sentiment coverage.
- Overall mood: 15.1% Positive, 81.5% Negative, 3.5% Neutral; sentiment index −33.6.
Key findings
- 6,847 comments analysed across UK programmes (2018–2025); overall sentiment is negative (index -33.6)
- The tone is negative in every segment assessed (overall index −33.6). Positives are relatively rare (15.1% of comments) against a heavy negative majority (81...
- Full-time students drive both volume (72.5% of all Workload comments) and the most negative tone (−37.2).
What students are saying in this category
- The tone is negative in every segment assessed (overall index −33.6). Positives are relatively rare (15.1% of comments) against a heavy negative majority (81.5%).
- Full-time students drive both volume (72.5% of all Workload comments) and the most negative tone (−37.2). Part-time students are less negative (−23.8) and more likely to be positive (21.2% vs 12.8% for full-time).
- Younger students (70.0% of comments) are notably more negative than mature students (−36.5 vs −27.2). Mature students are also more positive (18.4% vs 13.4%).
- By subject area (CAH1), sentiment is negative across the board, with more acute levels in Engineering & Technology (−39.0), Physical Sciences (−40.6), Architecture, Building & Planning (−42.2) and Mathematical Sciences (−42.7). Psychology (−26.7), Law (−25.3) and Combined/General Studies (−22.8) are less negative. Extremely small-group outliers exist (e.g., Agriculture at −13.7, n=10).
- Demographic patterns: female students are more negative than male (−35.6 vs −30.6). Black students show the lowest sentiment among ethnicity groupings (−38.4), with White at −33.0 and Asian at −34.9. Disabled students are slightly more negative than non-disabled (−36.4 vs −33.2).
Segments at a glance
| Segment |
Share % |
n |
Sentiment idx |
Positive % |
Negative % |
| Age: Young |
70.0 |
4,793 |
−36.5 |
13.4 |
83.4 |
| Age: Mature |
28.4 |
1,947 |
−27.2 |
18.4 |
77.4 |
| Mode: Full-time |
72.5 |
4,966 |
−37.2 |
12.8 |
83.9 |
| Mode: Part-time |
24.6 |
1,681 |
−23.8 |
21.2 |
75.0 |
| Mode: Apprenticeship |
1.2 |
85 |
−30.5 |
11.8 |
82.4 |
| Sex: Female |
62.2 |
4,257 |
−35.6 |
14.1 |
82.6 |
| Sex: Male |
36.1 |
2,472 |
−30.6 |
16.3 |
80.0 |
| Disability: Disabled |
18.9 |
1,294 |
−36.4 |
13.7 |
82.6 |
| Ethnicity: Black |
4.2 |
286 |
−38.4 |
12.9 |
85.0 |
| Ethnicity: White |
69.8 |
4,776 |
−33.0 |
15.3 |
81.3 |
By subject area (CAH1) — top volumes
| Subject group (CAH1) |
Share % |
n |
Sentiment idx |
Positive % |
| (CAH02) Subjects allied to medicine |
11.0 |
754 |
−35.8 |
13.5 |
| (CAH15) Social sciences |
7.5 |
514 |
−31.5 |
18.7 |
| (CAH10) Engineering and technology |
7.3 |
502 |
−39.0 |
12.2 |
| (CAH04) Psychology |
6.9 |
470 |
−26.7 |
19.8 |
| (CAH17) Business and management |
6.2 |
423 |
−29.7 |
16.3 |
| (CAH11) Computing |
5.3 |
365 |
−34.6 |
14.2 |
| (CAH20) Historical, philosophical and religious studies |
3.9 |
267 |
−29.1 |
16.9 |
| (CAH03) Biological and sport sciences |
3.9 |
264 |
−30.8 |
16.7 |
Notes:
- Most negative (among notable volumes): Mathematical Sciences (−42.7, n=176), Architecture/Building/Planning (−42.2, n=189), Physical Sciences (−40.6, n=221).
- Less negative clusters: Law (−25.3, n=234), Psychology (−26.7, n=470), Combined/General Studies (−22.8, n=216). Very small bases can swing indices.
What this means in practice
- Smooth and sequence workload at programme level
- Map all summative deadlines and heavy weeks across modules; avoid bunching; set escalation rules before adding/altering deadlines.
- Publish a single assessment calendar and lock a short “change window” ahead of key peaks.
- Make workload expectations explicit and check them with high-volume cohorts
- Provide time budgets for tasks (hours/week) and align with timetables; test clarity with full-time and younger students.
- Use short “workload check-ins” mid-term to catch overload early.
- Targeted support where tone is most negative
- Prioritise engineering/tech and the more negative CAH areas for timetable/assessment smoothing.
- Offer practical planning support to full-time, younger and Black student cohorts; monitor whether actions lift sentiment over subsequent cycles.
How Student Voice Analytics helps you
- Track workload sentiment over time and drill down from provider to school/department and programme, with demographic cuts (age, mode, sex, disability, ethnicity/domicile) and subject groupings (CAH).
- Produce concise, anonymised summaries and export-ready tables for rapid briefing, with like-for-like benchmarking capability by CAH code and key demographics when sector comparators are available.
Data at a glance (2018–2025)
- Volume: 6,847 comments on Workload (~1.8% of all comments).
- Coverage: 100% of category comments carry sentiment labels.
- Tone: 15.1% Positive, 81.5% Negative, 3.5% Neutral; sentiment index −33.6.
- Largest subgroups by volume: Full-time (72.5%), Young (70.0%), Female (62.2%), White (69.8%).