What UK Students Say About Communication with Supervisor, Lecturer, Tutor: NSS Feedback Analysis (6,373 Comments, 2018–2025)

Students’ comments on communication with supervisors, lecturers and tutors are narrowly positive overall, with clear variation by mode, age, disability and subject.

What are students saying in this category?

• The tone is mildly positive overall, and most comments come from full‑time (73.5%) and younger students (71.5%). Younger students are more upbeat (index +7.1) than mature students (+2.6).
• Communication lands less well for disabled students (+1.3) than for those not disabled (+7.1). Apprentices report the lowest tone (−14.6), pointing to challenges around availability, response times or channel fit for work‑based learners.
• International (not UK domiciled) students are notably positive (+13.5). Male students are a touch more positive than female (+7.9 vs +4.8), though both lean positive.
• Subject patterns vary: strong positivity in Physical Sciences (+19.3), Languages (+15.4), Historical/Philosophical studies (+13.5) and Biological/Sport sciences (+12.4). Tone is flatter or negative in Allied to Medicine (−7.5), Medicine & Dentistry (−10.6, smaller volume), Psychology (−0.7) and Creative/Performing Arts (−0.9). These differences suggest local practice and workload norms shape communication experiences.

Small‑n groups (e.g., Veterinary, n=4; sex unknown, n=6) are volatile and not emphasised below. Subject rows labelled “Unknown/Unspecified” are excluded from commentary.

Segment benchmarks (2018–2025)

Subject (CAH1) tone and volume (selected; n≥100)

Note: Medicine & Dentistry (CAH01) is also low‑toned (−10.6) but smaller in volume (n=86).

What this means in practice

• Set clear, programme‑wide service standards for academic communication
  • Define channels for different queries (VLE forum vs email vs office hours) and a simple “reply within X working days” norm.
  • Publish office hours and back‑up contacts for when supervisors are on leave or in clinics/labs.
• Fit communication to time‑poor cohorts
  • For apprentices and part‑time learners, use predictable, asynchronous updates (weekly digest, recorded briefings) and offer out‑of‑hours slots.
  • Summarise key actions/decisions after meetings in one place (VLE “source of truth”).
• Reduce barriers for disabled and mature students
  • Offer alternative modes (captioned recordings, written summaries) and confirm adjustments in writing.
  • Proactively schedule short check‑ins at key assessment or placement points.
• Close the loop on subjects with flatter/negative tone
  • In Allied to Medicine, Medicine/Dentistry, Psychology and Creative Arts: name a primary supervisor, standardise expectations for response times, and track missed responses.
  • Capture practices from high‑performing areas (e.g., Physical Sciences, Languages) and adapt them locally.
• Measure and learn fast
  • Track response‑time compliance and weekly communication issues by cohort; review at programme meetings and act within the next teaching block.

How Student Voice Analytics helps you

• See topic and sentiment for this communication theme over time, with drill‑downs by school/department, campus/site and cohort.
• Like‑for‑like comparisons across CAH subject groups and student demographics (age, domicile, mode, disability, commuter status), plus exports for programme boards and quick briefings.
• Concise, anonymised summaries highlight what to fix now and what to scale, avoiding anecdote‑driven decisions.

FAQs

• How is the “sentiment index” calculated?
  Per‑sentence sentiment is scored and aggregated to a category‑level index from −100 to +100.

• How are comments assigned to this topic?
  Each comment is tagged with one primary topic (here: communication with supervisors/lecturers/tutors). “Share % in category” figures above show the composition of this category by segment.

• Any cautions?
  Very small groups can swing sharply from a few comments; treat those as directional.

Data at a glance (2018–2025)

• Volume: 6,373 comments in this category; 100.0% with sentiment.
• Overall mood: 50.3% positive, 47.2% negative, 2.5% neutral; index +5.5.
• Largest shares within this category: Full‑time 73.5%; Young 71.5%; Not disabled 77.2%; White 67.4%; Female 60.9%.
• Mode gap: Apprentices −14.6 vs Full‑time +6.2 and Part‑time +5.5.
• Subject spread: Highest tone in Physical Sciences (+19.3) and Languages (+15.4); lowest in Allied to Medicine (−7.5) and Medicine & Dentistry (−10.6, smaller n).

How to use this data

This page presents sector-level student feedback analysis for the Communication with Supervisor, Lecturer, Tutor category (Academic support), 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.

Common subject areas linked to this theme (on our blog)

• politics
• management studies
• education
• adult nursing
• Pharmacy
• architecture
• mental health nursing
• journalism

Most-read posts in this category

• What improves student-staff communication in Education?
• Is academic staff communication working for management students?
• How can health sciences students communicate with academic staff?
• Do politics students benefit from structured communication?
• Do nursing students communicate effectively with academic staff?
• Are teacher training students getting the communication they need?
• How can design studies improve communication with students?
• How well do CS students communicate with academic staff?
• Do law students get the academic communication they need?
• Are staff-student communication gaps holding back medical education?

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.