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What UK Students Say About Remote Learning: NSS Feedback Analysis (12,933 Comments, 2018–2025)
Students’ comments on remote learning are slightly net‑negative overall, with stronger negativity among full‑time and younger cohorts. Part‑time and mature students are closer to neutral or mildly positive, and tone varies widely by subject area.
Key findings
- 12,933 comments analysed across UK programmes (2018–2025)
- Mode and life stage matter. Full‑time (index −11.2) and younger students (−10.2) are more negative; part‑time (+6.5) and mature (+3.5) are notably less negat...
- Differences by sex are small at scale (Female −1.9; Male −6.0). Disability status tracks the overall pattern closely (Not disabled −3.3; Disabled −3.9).
What students are saying in this category
- Mode and life stage matter. Full‑time (index −11.2) and younger students (−10.2) are more negative; part‑time (+6.5) and mature (+3.5) are notably less negative or mildly positive.
- Differences by sex are small at scale (Female −1.9; Male −6.0). Disability status tracks the overall pattern closely (Not disabled −3.3; Disabled −3.9).
- Domicile/ethnicity splits show the most negative tone among not‑UK‑domiciled students (−16.1; 2.2% share) and more neutral/positive tone among Asian students (+1.2).
- Subject mix is a major driver. Psychology (+4.5), Law (+5.2), Education (+5.2) and Computing (+2.6) read more positive; Design/Creative Arts (−17.0), Media (−15.3) and some Humanities (e.g., Historical/Philosophical/Religious Studies −10.1) are more negative. Treat small‑n groups with caution.
Segment benchmarks (2018–2025)
By mode and age
| Segment | Share % | n | Positive % | Negative % | Sentiment idx |
|---|---|---|---|---|---|
| Overall | 100.0 | 12,933 | 42.0 | 53.8 | −3.4 |
| Full‑time | 54.6 | 7,066 | 37.3 | 58.5 | −11.2 |
| Part‑time | 42.5 | 5,493 | 47.8 | 47.8 | +6.5 |
| Young | 49.4 | 6,394 | 37.9 | 57.7 | −10.2 |
| Mature | 48.4 | 6,257 | 45.9 | 49.9 | +3.5 |
Subject area (CAH1) — highest and lowest sentiment (n ≥ 100)
| Subject group (CAH1) | Share % | n | Sentiment idx |
|---|---|---|---|
| Law (CAH16) | 4.8 | 623 | +5.2 |
| Education and teaching (CAH22) | 3.0 | 384 | +5.2 |
| Psychology (CAH04) | 9.7 | 1,258 | +4.5 |
| Computing (CAH11) | 4.9 | 639 | +2.6 |
| Architecture, building and planning (CAH13) | 0.9 | 110 | +2.6 |
| — | |||
| Design, creative and performing arts (CAH25) | 2.1 | 268 | −17.0 |
| Historical/philosophical/religious studies (CAH20) | 4.1 | 525 | −10.1 |
| Social sciences (CAH15) | 9.9 | 1,275 | −6.0 |
| Physical sciences (CAH07) | 2.1 | 273 | −5.7 |
| Engineering and technology (CAH10) | 4.1 | 527 | −5.6 |
Notes: Sentiment index runs from −100 to +100. Percentages may not sum to 100% due to neutral content. Interpret very small groups cautiously.
What this means in practice
- Prioritise full‑time and younger cohorts: use a consistent weekly rhythm (same platform, day, joining route), shorter blocks (10–15 mins), and clearly signposted tasks.
- Make “remote‑first” materials standard: captioned recordings, transcripts, alt‑text, low‑bandwidth versions, and a single, stable link hub per module.
- Smooth the digital start: a short “getting set online” orientation for new cohorts; publish a one‑page “how we work online” playbook.
- Keep asynchronous parity: ensure every live session has a timely, searchable recording and a concise summary of key takeaways.
- Support international learners: time‑zone‑aware office hours, flexible deadlines, and written follow‑ups for critical announcements.
- For practice‑ or studio‑heavy subjects: invest in demo capture (multi‑angle where feasible), digital galleries/critique templates, and clear submission specs.
- Monitor and act weekly: track the top friction points (access, audio, link churn, timetable slips) and close the loop with a brief “what we fixed” update.
How Student Voice Analytics helps you
- Track topic volume and sentiment over time, with drill‑downs from provider level to school/department and cohort.
- Slice results by mode, age, domicile/ethnicity, disability and CAH subject groups for like‑for‑like comparisons.
- Produce concise, anonymised summaries for programme teams and governance.
- Export tables and charts for quick briefing and continuous improvement cycles.
How to use this data
This page presents sector-level student feedback analysis for the Remote Learning category (The teaching on my course), 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)
- media studies
- philosophy
- counselling, psychotherapy and occupational therapy
- Dentistry
- sport and exercise sciences
- health sciences (non-specific)
- physiotherapy
- Mechanical Engineering
Most-read posts in this category
- Can online psychology match the on-campus experience?
- What does remote learning mean for physiotherapy students?
- Can remote learning work for health sciences students?
- Does remote learning work for sport and exercise sciences?
- Remote learning in counselling, psychotherapy & OT
- Can midwifery students learn effectively through remote learning?
- What do accounting students think about remote learning?
- What do politics students say about remote learning?
- Are education students positive about remote learning?
- What are business studies students telling us about remote learning?
Recommended next steps
- Quantify: how often does this theme appear (and where)?
- Segment: by discipline (CAH/HECoS), level, mode, and cohort where appropriate.
- Benchmark: compare like-for-like to avoid cohort-mix artefacts.
- Act: define 1–3 changes, then track whether the theme shifts next cycle.
Cite this page
Student Voice AI (2025). "Remote Learning: NSS student feedback analysis (2018–2025)." Student Voice AI. https://www.studentvoice.ai/category/remote-learning/
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