What UK Medical Science Students Say: NSS Feedback Analysis (590 Comments, 2018–2025)
Key findings
- 590 comments analysed across UK medical science programmes (2018–2025); 49% positive overall
- Feedback is the most-discussed topic (9.1% of comments, sentiment index -31.6)
- Marking criteria is the biggest pain point (sentiment -56.4, -10.7 vs sector)
- Group size / SSRs is a clear strength (sentiment 58.0)
What students are saying
The Medical Sciences conversation is led by course design and assessment. Feedback is the single biggest topic (≈9.1% share) and is strongly negative (sentiment index −31.6), notably below the sector benchmark for the same topic. Marking criteria also draws a concentrated, very negative tone (−56.4). In contrast, Assessment methods (4.6% share) is less negative than sector, suggesting method choice itself is not the core problem—clarity, fairness and usefulness of feedback are.
Learning opportunities feature prominently and positively. Type and breadth of course content (8.7%) is well-regarded (index +21.8), and Module choice/variety (7.2%) is also positive. Where class size is mentioned (Group size/SSRs, 2.1%), tone is very strong (+58.0).
People and delivery remain consistent strengths. Teaching Staff (6.7%) is highly positive (+42.1, above sector), Delivery of teaching (5.1%) is net positive, and Student support (4.5%) is clearly appreciated (+23.4). Smaller but notable positives include Personal Tutor (+40.6) and Availability of teaching staff (+51.7).
The main friction is operational. Organisation and management of the course (5.5%) is deeply negative (−43.7), as are Scheduling/timetabling (−53.8) and Communication about course and teaching (−39.9). Student voice sentiment (2.6% share, −39.7) indicates many students do not feel their input leads to change. Collectively, this “delivery & ops” cluster accounts for roughly one-seventh of all comments and sits well below sector on tone.
Other notes. Placements/fieldwork/trips are barely mentioned (0.7%) and are positive when they appear. Remote learning is slightly positive (+4.9) and markedly better than sector. Career guidance/support attracts attention (4.3%) but is less positive than the sector average, and Library sentiment is also softer than sector.
Top categories by share (discipline vs sector)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Feedback | Assessment and feedback | 9.1 | 7.3 | 1.8 | -31.6 | -16.6 |
| Type and breadth of course content | Learning opportunities | 8.7 | 6.9 | 1.8 | 21.8 | -0.8 |
| Module choice / variety | Learning opportunities | 7.2 | 4.2 | 3.0 | 13.2 | -4.2 |
| Teaching Staff | The teaching on my course | 6.7 | 6.7 | -0.1 | 42.1 | 6.6 |
| Organisation, management of course | Organisation and management | 5.5 | 3.3 | 2.1 | -43.7 | -29.7 |
| Delivery of teaching | The teaching on my course | 5.1 | 5.4 | -0.3 | 10.4 | 1.7 |
| Assessment methods | Assessment and feedback | 4.6 | 3.0 | 1.6 | -13.3 | 10.4 |
| Student support | Academic support | 4.5 | 6.2 | -1.8 | 23.4 | 10.2 |
| Marking criteria | Assessment and feedback | 4.5 | 3.5 | 0.9 | -56.4 | -10.7 |
| Career guidance, support | Learning community | 4.3 | 2.4 | 1.9 | 13.8 | -16.3 |
Most negative categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Marking criteria | Assessment and feedback | 4.5 | 3.5 | 0.9 | -56.4 | -10.7 |
| Scheduling/ timetabling | Organisation and management | 2.2 | 2.9 | -0.6 | -53.8 | -37.3 |
| Organisation, management of course | Organisation and management | 5.5 | 3.3 | 2.1 | -43.7 | -29.7 |
| Communication about course and teaching | Organisation and management | 2.4 | 1.7 | 0.7 | -39.9 | -4.1 |
| Student voice | Student voice | 2.6 | 1.8 | 0.8 | -39.7 | -20.5 |
| Feedback | Assessment and feedback | 9.1 | 7.3 | 1.8 | -31.6 | -16.6 |
| COVID-19 | Others | 3.8 | 3.3 | 0.4 | -16.9 | 16.0 |
Shares are the proportion of all Medical Sciences comments whose primary topic is the category. Sentiment index ranges from −100 (more negative than positive) to +100 (more positive than negative).
Most positive categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Group size / SSRs | Learning opportunities | 2.1 | 0.4 | 1.6 | 58.0 | 43.0 |
| Teaching Staff | The teaching on my course | 6.7 | 6.7 | -0.1 | 42.1 | 6.6 |
| Student support | Academic support | 4.5 | 6.2 | -1.8 | 23.4 | 10.2 |
| Student life | Learning community | 2.6 | 3.2 | -0.6 | 22.3 | -9.8 |
| Type and breadth of course content | Learning opportunities | 8.7 | 6.9 | 1.8 | 21.8 | -0.8 |
| Career guidance, support | Learning community | 4.3 | 2.4 | 1.9 | 13.8 | -16.3 |
| Module choice / variety | Learning opportunities | 7.2 | 4.2 | 3.0 | 13.2 | -4.2 |
What this means in practice
Start with assessment clarity. Publish annotated exemplars, checklist-style rubrics, and concise marking briefs that show “what good looks like.” Calibrate marking across modules and set a realistic feedback SLA, then report on turnaround performance openly. These steps address the core drivers behind negative Feedback and Marking criteria sentiment.
Stabilise the operational rhythm. Name an owner for scheduling and course organisation, implement a timetable “freeze” window, and issue a weekly change log to a single source of truth. Bundle key updates (what changed and why) and hold a short, regular Q&A slot. This directly targets Organisation & management, Scheduling, and course Comms.
Close the loop on student voice. Publish “You said, we did” updates each term and give visibility to decisions that cannot be implemented (with reasons). Invite a small rotating panel of students to preview changes before roll‑out to strengthen perceived responsiveness.
Protect the positives. Maintain visibility of teaching availability and the supportive culture that underpins strong Teaching Staff, Student support and Personal Tutor sentiment. For careers, integrate short, practical guidance points within modules and signpost next steps clearly.
Data at a glance (2018–2025)
- Top topics by share: Feedback (≈9.1%), Type & breadth of course content (≈8.7%), Module choice/variety (≈7.2%), Teaching Staff (≈6.7%), Organisation & management of course (≈5.5%).
- Delivery & ops cluster (placements, scheduling, organisation, course comms, remote learning): ≈14.1% of comments, with notably negative tone driven by Scheduling (−53.8), Organisation & management (−43.7) and Comms (−39.9).
- People & growth cluster (personal tutor, student support, teaching staff, delivery of teaching, personal development, student life): ≈23.7% of comments, generally positive (e.g., Teaching Staff +42.1; Student support +23.4).
- Relative to sector attention: above-sector focus on Module choice/variety (+3.0 pp), Organisation & management (+2.1 pp), Feedback (+1.8 pp), and Career guidance (+1.9 pp). Under‑discussed vs sector: Placements (−2.7 pp), Personal Tutor (−1.5 pp), Learning resources (−2.2 pp).
- How to read the numbers. Each comment is assigned one primary topic; share is that topic’s proportion of all comments. Sentiment is scored per sentence and summarised as an index from −100 (more negative) to +100 (more positive), then averaged at category level.
How Student Voice Analytics helps you
Student Voice Analytics turns open-text survey data into clear, prioritised actions. It tracks topics and sentiment over time so you can see which issues are rising or falling across the whole institution as well as within specific departments and schools.
You can evidence impact with like-for-like sector comparisons across CAH codes and by demographics (e.g., year of study, domicile, mode of study, campus/site, commuter status). Segment results by site/provider, cohort and year to target interventions precisely. The platform also generates concise, anonymised summaries and representative comments for partners and programme teams, and provides export-ready outputs (web, slides, dashboard) to share priorities and progress quickly.
How to use this data
This page presents sector-level student feedback analysis for Medical Science, with sentiment benchmarks and topic breakdowns you can reference directly in institutional documents.
Use this for
- Annual Programme Review (APR) — reference the top-categories table and sentiment benchmarks to contextualise your programme's results against the discipline.
- TEF and quality enhancement — cite the sentiment index and sector delta columns as evidence of awareness of student priorities relative to the sector.
- Professional body revalidation — draw on placement, assessment and support data for evidence of responsiveness to student feedback in your discipline.
- Staff-Student Liaison Committees (SSLCs) — share the key findings and most-negative categories as discussion starters with student representatives.
- New programme design — use the topic share and sentiment data to anticipate which aspects of the student experience will need proactive attention.
Common themes in this subject area (on our blog)
Most-read posts in this subject area
- What does feedback tell us about medical sciences teaching?
- Can better feedback and organisation improve medical student learning?
Recommended next steps
- Look for repeatability: which themes recur across years and modules?
- Check whether issues are structural (resources/staffing) or local (one module/team).
- Define what “good” looks like for the subject (examples, rubrics, assessment clarity).
- Track movement: do actions reduce volume/negativity for key themes next cycle?
Cite this page
Student Voice AI (2025). "Medical Science student feedback analysis (CAH01-01-01)." Student Voice AI. https://www.studentvoice.ai/cah3/medical-sciences-(non-specific)/
