What are students actually saying about Personal Tutor (NSS 2018–2025)?

Students are broadly positive about personal tutoring. The tone is consistently warm across demographics, with notably higher scores among full-time students and in several CAH subject groups.

Scope: UK NSS open-text comments for Personal Tutor across academic years 2018–2025.
Volume: ~11,840 comments; 100% with sentiment captured.
Overall mood: 61.7% Positive, 36.0% Negative, 2.3% Neutral (positive:negative ≈ 1.71:1).
Sentiment index: +27.1 (−100 to +100).

What are students saying in this category?

• The balance of opinion is positive overall, with nearly two in three comments upbeat about their personal tutor experience.
• Mode matters: full-time students are more positive than part-time (index +32.0 vs +22.4), suggesting the model works best for students on a standard rhythm.
• Age shows a smaller gap: young students are slightly more positive than mature (index +28.9 vs +25.6).
• Disabled and non-disabled students report similar tones (indices +27.9 vs +26.8), indicating broadly inclusive experiences.
• By subject area, sentiment is strongest in allied to medicine (+43.4) and physical sciences (+41.4), while computing (+18.0) and engineering (+15.5) are softer. Large-volume groups (e.g., psychology and social sciences) sit in the low-to-mid 20s.

Benchmarks by student segment

Notes: Percentages and indices are rounded to 1 decimal. Indices range from −100 to +100.

Largest subject groups (CAH) by volume

Additional context: Physical sciences (+41.4) and geography/earth/environmental studies (+36.9) are also strong; computing (+18.0) and engineering/technology (+15.5) are comparatively lower.

What this means in practice

• Protect the model for part-time students
  • Offer predictable, flexible touchpoints (e.g., out-of-hours slots and asynchronous options).
  • Publish a simple service standard (expected response window, typical check-in cadence) and track adherence.
• Close small gaps by age
  • Ensure mature students can access timely support without navigating multiple routes; make “who to contact for what” obvious.
  • Use quick‑start guides and case-based FAQs at course start and assessment points.
• Share playbooks across subjects
  • Lift lower‑scoring areas (e.g., engineering, computing) by adopting practices from higher‑scoring subjects (e.g., allied to medicine, physical sciences): clear onboarding, consistent check‑ins, and proactive outreach at key stress points.
• Monitor parity
  • Maintain the positive experience reported by disabled students and across ethnic groups by reviewing accessibility of communications and appointment options.
  • Track the mode and age gaps (currently ~9.6 index points for mode; ~3.3 for age) and review monthly.

How Student Voice Analytics helps you

• See topic and sentiment for Personal Tutor across years, and drill down from provider to school/department and course.
• Compare like-for-like across CAH groups and student demographics (age, domicile, mode, campus/site), and segment by cohort or year where available.
• Generate concise, anonymised summaries for programme teams, with export-ready tables and year-on-year movement to evidence change.

Data at a glance (2018–2025)

• Volume: ~11,840 comments; 100% with sentiment captured.
• Overall mood: 61.7% Positive, 36.0% Negative, 2.3% Neutral (≈1.71:1 positive:negative).
• Largest subgroups by volume: mature (52.1%) and part-time (50.5%).
• Spread by subject (CAH) ranges from mid-teens to low-40s on the sentiment index.