Student Voice Analytics for Medicine — UK student feedback 2018–2025
Scope. UK NSS open-text comments for Medicine (CAH01-01-02) students across academic years 2018–2025.
Volume. ~8,780 comments; 97.6% successfully categorised to a single primary topic.
Overall mood. Roughly 51.5% Positive, 44.9% Negative, 3.7% Neutral (positive:negative ≈ 1.15:1).
What students are saying
Medicine students talk first about practice-facing learning. Placements/fieldwork account for about one in six comments (≈16.8% share) and, unlike many disciplines, are on balance positive (sentiment index ~+12.0, roughly at sector). Students also respond well to the delivery of teaching (index ~+15.5, above sector), and they value the type and breadth of course content (index ~+29.0). Comments about teaching staff are notably warm (index ~+39.2), and “student life” is similarly positive.
The operational rhythm of the course remains a drag on overall tone. Organisation and management (index ~−19.2), scheduling/timetabling (index ~−33.5), and course communications (index ~−43.4) attract sustained negative sentiment and appear more frequently here than in the sector. Assessment and feedback issues are a clear second source of friction: feedback (index ~−27.1), assessment methods (index ~−32.6) and marking criteria (index ~−45.1) cluster together as areas where expectations and processes need to be clearer.
Two further notes. First, “student support” is talked about often (6.9% share) but trends a little negative for this cohort (index ~−2.7) and sits well below a typically positive sector baseline. Second, several topics are less present than in the wider sector—remote learning and module choice are relatively minor in the Medicine conversation—reinforcing that students’ day‑to‑day experience is anchored in delivery, operations and assessment.
Top categories by share (Medicine vs sector):
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Placements/ fieldwork/ trips | Learning opportunities | 16.8 | 3.4 | 13.3 | 12.0 | 0.2 |
| Delivery of teaching | The teaching on my course | 8.7 | 5.4 | 3.2 | 15.5 | 6.7 |
| Student support | Academic support | 6.9 | 6.2 | 0.7 | -2.7 | -15.9 |
| Type and breadth of course content | Learning opportunities | 6.3 | 6.9 | -0.6 | 29.0 | 6.4 |
| Organisation, management of course | Organisation and management | 5.6 | 3.3 | 2.2 | -19.2 | -5.2 |
| Student voice | Student voice | 5.3 | 1.8 | 3.6 | -33.7 | -14.4 |
| Feedback | Assessment and feedback | 5.1 | 7.3 | -2.2 | -27.1 | -12.1 |
| Assessment methods | Assessment and feedback | 4.6 | 3.0 | 1.6 | -32.6 | -8.8 |
| Scheduling/ timetabling | Organisation and management | 4.0 | 2.9 | 1.1 | -33.5 | -17.0 |
| Teaching Staff | The teaching on my course | 3.9 | 6.7 | -2.8 | 39.2 | 3.7 |
Most negative categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Marking criteria | Assessment and feedback | 2.3 | 3.5 | -1.2 | -45.1 | 0.6 |
| Communication about course and teaching | Organisation and management | 3.9 | 1.7 | 2.2 | -43.4 | -7.6 |
| Student voice | Student voice | 5.3 | 1.8 | 3.6 | -33.7 | -14.4 |
| Scheduling/timetabling | Organisation and management | 4.0 | 2.9 | 1.1 | -33.5 | -17.0 |
| Assessment methods | Assessment and feedback | 4.6 | 3.0 | 1.6 | -32.6 | -8.8 |
| Feedback | Assessment and feedback | 5.1 | 7.3 | -2.2 | -27.1 | -12.1 |
| COVID-19 | Others | 2.3 | 3.3 | -1.1 | -23.4 | 9.5 |
Shares are the proportion of all Medicine 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 |
|---|---|---|---|---|---|---|
| Student life | Learning community | 3.8 | 3.2 | 0.6 | 39.4 | 7.3 |
| Teaching Staff | The teaching on my course | 3.9 | 6.7 | -2.8 | 39.2 | 3.7 |
| Type & breadth of course content | Learning opportunities | 6.3 | 6.9 | -0.6 | 29.0 | 6.4 |
| Delivery of teaching | The teaching on my course | 8.7 | 5.4 | 3.2 | 15.5 | 6.7 |
| Placements/fieldwork/trips | Learning opportunities | 16.8 | 3.4 | 13.3 | 12.0 | 0.2 |
What this means in practice
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Stabilise the delivery engine. Publish a clear schedule “freeze” window, explain any late changes and their rationale, and keep a single source of truth for course communications. Naming an operational owner and sending a short weekly update will reduce avoidable friction.
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Make assessment predictable and legible. Provide annotated exemplars, checklist-style rubrics, and realistic turnaround times. Align feedback to criteria and show how students can close the gap—especially where “assessment methods” and “marking criteria” are pain points.
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Close the loop on student voice. When students raise issues, acknowledge, act and report back. A light, regular “you said/we’re doing” update will lift sentiment in Student Voice, Organisation/Management and Comms simultaneously.
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Protect and amplify existing strengths. Keep the positive momentum in delivery of teaching, placements and the breadth of content by sharing good practice across modules and teams.
Data at a glance (2018–2025)
- Top topics by share: Placements/fieldwork (≈16.8%), Delivery of teaching (≈8.7%), Student support (≈6.9%), Type and breadth of course content (≈6.3%), Organisation & management of course (≈5.6%), Student voice (≈5.3%).
- Cluster view:
- Delivery & ops cluster (placements, scheduling, organisation, course comms, remote learning): ≈31.1% of all comments, with tone mixed to negative across the ops elements.
- People & growth cluster (personal tutor, student support, teaching staff, availability of staff, delivery of teaching, personal development, student life): ≈26.8% of comments, generally positive.
- How to read the numbers. Each comment is assigned one primary topic; share is that topic’s proportion of all comments. Sentiment is calculated per sentence and summarised as an index from −100 to +100, then averaged at category level.
How Student Voice Analytics helps you
Student Voice Analytics turns open-text survey comments into clear, prioritised actions. It tracks topics, sentiment and movement by year for every discipline, letting leaders see patterns at whole‑institution level and drill down to faculties, schools and programmes.
It also enables like‑for‑like evaluation: you can run sector comparisons across CAH codes and by demographics (e.g., year of study, domicile, mode of study, campus/site, commuter status), and segment by site/provider, cohort and year. Concise, anonymised theme summaries and representative comments make it easy to brief programme teams and external partners without reading thousands of responses. Export‑ready outputs (for web, decks and dashboards) help you share priorities and progress quickly across the organisation.
