What UK Students Say About Non-academic Staff: NSS Feedback Analysis (578 Comments, 2018–2025)

Students are broadly positive about their interactions with non-academic staff. Overall tone is favourable, but there are clear differences by subject area, gender, study mode and ethnicity that point to uneven experiences.

Sentiment index: +24.7 (−100 to +100 scale).

What students are saying in this category

• The majority of comments are positive about professionalism and helpfulness, but around one‑third are negative, indicating room to tighten consistency.
• Most feedback comes from full‑time (91.9%) and younger students (83.0%). Mature learners are slightly more positive (index +28.6) than younger students (+24.0).
• Tone is notably more positive among female students (+30.5) than male students (+14.9).
• Part‑time students report a weaker experience (+15.6) than full‑time students (+25.5), suggesting access or responsiveness gaps outside standard hours.
• Subject variation is pronounced: very positive in design/creative arts (+58.7) and engineering (+50.7); distinctly negative in computing (−35.1) and below zero in subjects allied to medicine (−13.7) and historical/philosophical studies (−12.8).
• Ethnicity patterns: White students (58.5% of comments) are more positive (+29.8) than Asian (+16.9) and not UK‑domiciled (+11.4) students. Several minority group bases are small; interpret with care.

Segmentation and benchmarks

Indices reflect the balance of positive vs negative sentiment (−100 to +100). Shares are within this category.

Demographic and mode snapshot

Subject area (CAH1) extremes (n ≥ 18)

Note: Several smaller CAH groups (<18 comments) also show strong tones (e.g., law +44.4, education +77.8), but bases are small.

What should we fix first?

1. Close the consistency gap across subjects

   • Pair each school/department with a named non-academic staff liaison.
   • Build a lightweight knowledge base of course‑specific queries and resolutions; review monthly in low‑index areas (computing; allied to medicine).

2. Strengthen service access for part‑time and male students

   • Offer predictable out‑of‑hours windows and published response SLAs for common requests.
   • Track service satisfaction by mode and gender; follow up on low‑tone interactions within 5 working days.

3. Standardise “front‑of‑house” service behaviours

   • Use a simple triage script (clarify issue, set expectation, confirm owner, give next step).
   • Provide a single contact route per query type with clear hand‑offs; avoid duplicate tickets.

4. Capture and spread what works

   • Lift practices from high‑performing subject areas (design/creative arts; engineering) into an internal playbook with examples and templates.

How Student Voice Analytics helps you

• Surfaces category‑level sentiment and volume over time, with drill‑downs by provider, school/department, campus/site and cohort.
• Like‑for‑like comparisons across CAH subject groups and demographics (age, domicile, mode, sex, disability) to target interventions where tone is weakest.
• Concise, anonymised summaries and export‑ready outputs for briefing professional services teams and programme leads.

FAQs

• How is the sentiment index calculated?
  We score per‑sentence sentiment and summarise to an index from −100 to +100, then average within the category.

• What do “shares” represent here?
  Share % shows the proportion of this category’s comments attributed to a segment (e.g., full‑time vs part‑time) across 2018–2025.

Data at a glance (2018–2025)

• Volume: 578 comments; 100.0% with sentiment.
• Overall mood: 61.4% Positive, 34.6% Negative, 4.0% Neutral (index +24.7).
• Largest segments by share: Full‑time (91.9%), Young (83.0%), Female (63.1%), White (58.5%).

How to use this data

This page presents sector-level student feedback analysis for the Non-academic Staff category (Others), 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.

Recommended next steps

1. Quantify: how often does this theme appear (and where)?
2. Segment: by discipline (CAH/HECoS), level, mode, and cohort where appropriate.
3. Benchmark: compare like-for-like to avoid cohort-mix artefacts.
4. Act: define 1–3 changes, then track whether the theme shifts next cycle.