Student Voice Analytics for Biomedical Sciences Non-specific — UK student feedback 2018–2025

Scope. UK NSS open-text comments for Biomedical Sciences (CAH02-05-03) across academic years 2018–2025.
Volume. ~4,049 comments; 98% successfully categorised to a single primary topic.
Overall mood. Roughly 51.0% Positive, 45.8% Negative, 3.3% Neutral (positive:negative ≈ 1.11:1).

What students are saying

The strongest signal in Biomedical Sciences is about assessment. Just under a quarter of all comments focus on Assessment & Feedback (≈22.8% across Feedback, Marking criteria, Assessment methods and Dissertation). Within that, Feedback is the single largest topic (10.6% share) and is strongly negative (index −31.5, well below the sector by −16.4). Students also report very negative experiences with Marking criteria (−52.3) and critical views of Assessment methods (−25.5). In contrast, Dissertation sentiment sits on the positive side (index +5.6) and is notably above the sector baseline (+16.2), suggesting clearer expectations and support for project work are landing better than for taught-module assessment.

Beyond assessment, the academic experience itself is described positively. Delivery of teaching (+14.8) and Teaching Staff (+34.8) are well-regarded. Students rate the Type and breadth of course content (+29.3) and Module choice/variety (+29.3) highly, with the latter substantially more positive than the sector (+11.9). Supportive people and structures also stand out: Student support (+12.5), Availability of teaching staff (+41.4), and Personal Tutor (+48.0) carry strong positive tone, albeit Personal Tutor is mentioned less often than in the wider sector by share.

Operational delivery is a smaller part of the conversation but carries risk when it appears. Organisation and management of the course (−16.1), Scheduling/timetabling (−30.5), and Remote learning (−16.7) all trend negative. Communication about course and teaching is also more negative than positive (−29.9), though it attracts fewer comments. Placements/fieldwork are relatively marginal in this discipline (1.3% share, well below sector), and sentiment is near neutral (+7.7).

Top categories by share (discipline vs sector)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Feedback	Assessment and feedback	10.6	7.3	+3.3	−31.5	−16.4
Type and breadth of course content	Learning opportunities	6.8	6.9	−0.2	+29.3	+6.7
Student support	Academic support	6.1	6.2	−0.1	+12.5	−0.7
Delivery of teaching	The teaching on my course	6.1	5.4	+0.6	+14.8	+6.0
Teaching Staff	The teaching on my course	6.0	6.7	−0.7	+34.8	−0.7
Marking criteria	Assessment and feedback	5.2	3.5	+1.7	−52.3	−6.7
COVID-19	Others	4.9	3.3	+1.5	−26.9	+6.1
Assessment methods	Assessment and feedback	4.3	3.0	+1.3	−25.5	−1.7
Organisation, management of course	Organisation and management	3.8	3.3	+0.4	−16.1	−2.2
Module choice / variety	Learning opportunities	3.4	4.2	−0.7	+29.3	+11.9

Most negative categories (share ≥ 2%)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Marking criteria	Assessment and feedback	5.2	3.5	+1.7	−52.3	−6.7
Workload	Organisation and management	2.0	1.8	+0.1	−41.3	−1.3
Feedback	Assessment and feedback	10.6	7.3	+3.3	−31.5	−16.4
Scheduling/timetabling	Organisation and management	2.1	2.9	−0.8	−30.5	−14.0
COVID-19	Others	4.9	3.3	+1.5	−26.9	+6.1
Assessment methods	Assessment and feedback	4.3	3.0	+1.3	−25.5	−1.7
Student voice	Student voice	2.3	1.8	+0.6	−23.3	−4.1

Most positive categories (share ≥ 2%)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Personal Tutor	Academic support	2.0	3.2	−1.2	+48.0	+29.3
Availability of teaching staff	Academic support	2.5	2.1	+0.4	+41.4	+2.1
Teaching Staff	The teaching on my course	6.0	6.7	−0.7	+34.8	−0.7
Student life	Learning community	2.6	3.2	−0.5	+34.0	+1.9
Type and breadth of course content	Learning opportunities	6.8	6.9	−0.2	+29.3	+6.7
Module choice / variety	Learning opportunities	3.4	4.2	−0.7	+29.3	+11.9
Career guidance, support	Learning community	3.1	2.4	+0.7	+24.5	−5.6

What this means in practice

Make assessment clarity a design priority. Publish annotated exemplars, plain‑English marking criteria and checklist‑style rubrics; align briefings, in‑class calibration and Q&A to those artefacts. Commit to realistic, visible turnaround times and ensure feedback is specific and forward‑looking. These steps directly address the heaviest, most negative topics (Feedback, Marking criteria, Assessment methods).
Stabilise the operational rhythm. Name a single source of truth for course communications, schedule a predictable weekly update, and clearly own timetable and change decisions. This reduces avoidable friction in Scheduling, Organisation and Remote learning.
Double down on the people strengths. Protect time for Personal Tutors and teaching staff availability; make those touchpoints easy to find and consistent across modules. Where sentiment is already strong, small reliability gains compound quickly.
Keep the good parts of project support. Dissertation sentiment is comparatively positive—codify what’s working (milestones, supervision patterns, exemplars) and reuse it in taught modules.

Data at a glance (2018–2025)

Top topics by share: Feedback (≈10.6%), Type & breadth of course content (≈6.8%), Student support (≈6.1%), Delivery of teaching (≈6.1%), Teaching Staff (≈6.0%).
Clusters by share:
- Assessment & feedback cluster (Feedback, Marking criteria, Assessment methods, Dissertation): ≈22.8%.
- Delivery & ops cluster (Placements/fieldwork, Scheduling, Organisation & management, Course comms, Remote learning): ≈12.2%.
- People & growth cluster (Personal Tutor, Student support, Teaching Staff, Availability of teaching staff, Delivery of teaching, Personal development, Student life): ≈27.2%.
Topics under- or over‑discussed vs sector: Placements/fieldwork are less prominent here (≈1.3% vs 3.4% sector). Module choice/variety attracts fewer comments by share than sector yet is markedly more positive on tone. Student voice is mentioned a little more than sector by share but is notably negative.
How to read the numbers. Each comment is assigned one primary topic; share is that topic’s proportion of all comments. Sentiment is summarised as an index from −100 (more negative than positive) to +100 (more positive than negative), then averaged at category level.

How Student Voice Analytics helps you

Student Voice Analytics turns open‑text survey comments into clear priorities you can act on. It tracks topics and sentiment over multiple years, so you can see which categories are driving change and where to intervene next—at whole‑institution level and right down to schools, departments and programmes.

It enables like‑for‑like sector comparisons across CAH codes and by demographics (e.g., year of study, domicile, mode of study, campus/site, commuter status), so you can evidence improvement against the right peer group. You can segment by site/provider, cohort and year to target action where it will move sentiment most. The platform also produces concise, anonymised theme summaries and representative comments for partners and programme teams, and offers export‑ready outputs (web, slide, dashboard) to share priorities and progress across the institution.