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%).

How to use this data

This page presents sector-level student feedback analysis for the Workload category (Organisation and management), with demographic and subject-area benchmarks you can reference directly in institutional documents.

Use this for

Annual Programme Review (APR) — reference the segment benchmarks to contextualise your programme's feedback patterns against the sector.
TEF and quality enhancement — cite the demographic breakdowns and subject-area sentiment as evidence of awareness of differential student experience.
Equality, diversity and inclusion (EDI) — use the ethnicity, disability and age segment data to evidence where feedback experience differs by student group.
Staff-Student Liaison Committees (SSLCs) — share the key findings and subject-area table as discussion starters with student representatives.
Action planning — use the "What this means in practice" recommendations as a starting point for targeted interventions.