What are students actually saying about Non-academic staff (NSS 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.

Scope: UK NSS open‑text comments tagged to the category “Non-academic staff” across academic years 2018–2025.
Volume: ~578 comments; 100.0% with sentiment.
Overall mood: 61.4% Positive, 34.6% Negative, 4.0% Neutral (positive:negative ≈ 1.8:1).
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

Group Share % N Sentiment idx
Age — Young 83.0 480 24.0
Age — Mature 15.6 90 28.6
Sex — Female 63.1 365 30.5
Sex — Male 35.5 205 14.9
Mode — Full-time 91.9 531 25.5
Mode — Part-time 6.1 35 15.6
Disability — Disabled 24.6 142 27.3
Disability — Not disabled 74.2 429 24.1

Subject area (CAH1) extremes (n ≥ 18)

Subject area (CAH1) Share % N Sentiment idx
design, and creative and performing arts (CAH25) 14.0 81 58.7
engineering and technology (CAH10) 4.7 27 50.7
biological and sport sciences (CAH03) 3.5 20 32.8
physical sciences (CAH07) 3.1 18 31.4
computing (CAH11) 3.1 18 −35.1
subjects allied to medicine (CAH02) 6.2 36 −13.7
historical, philosophical and religious studies (CAH20) 3.3 19 −12.8
social sciences (CAH15) 9.0 52 −0.2

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%).

Subject specific insights on "non-academic staff"

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