What are students actually saying about Workload (NSS 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.

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

By subject area (CAH1) — top volumes

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

1. 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.

2. 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.

3. 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%).