What UK Students Say About Internationalisation: NSS Feedback Analysis (220 Comments, 2018–2025)

Overall tone on Internationalisation is mildly negative, with wide variation by demographic and subject. White students are notably positive, while Asian and Black students are much more negative. Subject areas also diverge: business and social sciences lean neutral-to-positive, whereas allied to medicine and mathematical sciences skew negative.

Sentiment index: −4.4.

What are students saying in this category?

• The headline is a near balance of views but leaning negative. Most comments are from full‑time students (97.7%); part‑time volumes are too small to interpret.
• Age matters: young students sit close to neutral (index −2.3) while mature students are considerably more negative (−18.1).
• Ethnicity shows the clearest split. White students are positive overall (index +21.5). Asian (−22.6) and Black students (−39.5) report more negative experiences and, given their volumes, drive a substantial share of the negativity.
• By subject area, the largest groups cluster around neutral: business and management (−1.6) and social sciences (+7.0). Negativity is stronger in allied to medicine (−22.7) and mathematical sciences (−28.7). A few smaller subjects show very negative tone (e.g., medicine and dentistry; education and teaching), but counts are low and should be treated with caution.
• Gender differences are modest: both female (−5.9) and male (−2.3) groups lean mildly negative.

Trend & benchmarks

This extract aggregates 2018–2025. No year-by-year or sector comparators are included here.

Demographic view (2018–2025 aggregated)

Subject view (CAH1; top areas by volume, n≥7; 2018–2025 aggregated)

Note: Counts under 10 should be interpreted cautiously.

What this means in practice

• Close the tone gap where it is largest. Prioritise Asian and Black students’ experience and mature students by running targeted listening sessions, capturing top recurring issues, and closing the loop publicly with time‑bound actions.
• Make internationalisation tangible in teaching and student life. Ensure examples, case studies and activities reflect global perspectives and support cross‑cultural interaction, with clear guidance on expectations for group work and participation.
• Strengthen transition and academic literacies. Offer early, opt‑in micro‑supports on academic conventions, assessment expectations and communication clarity; signpost consistently in induction and through the first term.
• Focus subject hotspots. Partner with programme teams in allied to medicine and mathematical sciences to review where students feel least included or supported; implement small, testable changes and track movement in the sentiment index.
• Monitor by cohort and demographics. Use a simple dashboard to track comment volumes and sentiment by ethnicity and age each term, aiming to lift the overall index above zero and narrow the largest gaps.

How Student Voice Analytics helps you

• See topic and sentiment for Internationalisation over time, with drill‑downs from provider to school/department and programme.
• Benchmark like‑for‑like across CAH areas and by demographics (domicile, ethnicity, age, mode, site/campus), and compare cohorts.
• Produce concise, anonymised summaries for programme and EDI leads, with export‑ready tables and charts for boards and committees.

Data at a glance (2018–2025)

• Volume: 220 comments; 100.0% with sentiment.
• Overall mood: 41.8% Positive, 50.9% Negative, 7.3% Neutral; sentiment index −4.4.
• Largest gaps: Asian (−22.6) and Black (−39.5) groups; mature students (−18.1).
• Subject hotspots: allied to medicine (−22.7), mathematical sciences (−28.7); neutral‑to‑positive in business (−1.6), social sciences (+7.0), law (+11.1).

How to use this data

This page presents sector-level student feedback analysis for the Internationalisation 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.