Teaching staff shine — what 966 veterinary students report

Scope. UK NSS open-text comments for Veterinary Medicine and Dentistry (CAH05-01-01) students across academic years 2018–2025.
Volume. ~966 comments; 96.3% successfully categorised to a single primary topic.
Overall mood. Roughly 57.0% Positive, 40.3% Negative, 2.7% Neutral (positive:negative ≈ 1.41:1).

What students are saying

Students talk most about the quality of teaching and course design. Comments about Teaching Staff (about one in ten, 9.8%) are strongly positive (sentiment index around +56.0) and well above sector. Delivery of teaching (7.7%) also carries a notably positive tone (+36.9, markedly higher than sector), and Type and breadth of course content (9.0%) is viewed positively overall. Community and support aspects are similarly strong: Student life (6.9%, +51.1), Student support (6.8%, +22.5), and Personal development (2.5%, +43.9) are consistent positives. Facilities indicators also stand out: General facilities (2.2%) are exceptionally positive (+74.0).

Balancing those strengths, operational delivery is the main friction. Organisation and management of the course (8.2%) trends negative (−16.9), and Scheduling/timetabling (7.1%) is the lowest-scoring category (−44.5), well below sector. Communication about course and teaching (2.3%, −36.6) and Student voice (4.8%, −31.4) reinforce a clear message: students want predictability, a single source of truth, and visible ownership of decisions.

Assessment and feedback remain challenging. Feedback (5.9%) is negative (−19.1), and Assessment methods (2.6%) also score low (−29.2). Marking criteria is mentioned less often but is sharply negative when it appears (−46.8). Placements/fieldwork/trips feature less frequently than the sector (2.5% vs 3.4%) and sit slightly negative (−8.9). COVID‑19 still appears (3.5%) but is less negative than the sector baseline.

Top categories by share (Veterinary Medicine and Dentistry vs sector)

Category Section Share % Sector % Δ pp Sentiment idx Δ vs sector
Teaching Staff The teaching on my course 9.8 6.7 3.0 56.0 20.5
Type and breadth of course content Learning opportunities 9.0 6.9 2.1 27.9 5.3
Organisation & management of course Organisation and management 8.2 3.3 4.8 −16.9 −2.9
Delivery of teaching The teaching on my course 7.7 5.4 2.3 36.9 28.1
Scheduling/timetabling Organisation and management 7.1 2.9 4.2 −44.5 −27.9
Student life Learning community 6.9 3.2 3.7 51.1 19.0
Student support Academic support 6.8 6.2 0.6 22.5 9.3
Feedback Assessment and feedback 5.9 7.3 −1.4 −19.1 −4.1
Student voice Student voice 4.8 1.8 3.1 −31.4 −12.1
COVID-19 Others 3.5 3.3 0.2 −18.1 14.8

Most negative categories (share ≥ 2%)

Category Section Share % Sector % Δ pp Sentiment idx Δ vs sector
Scheduling/timetabling Organisation and management 7.1 2.9 4.2 −44.5 −27.9
Communication about course and teaching Organisation and management 2.3 1.7 0.6 −36.6 −0.8
Student voice Student voice 4.8 1.8 3.1 −31.4 −12.1
Assessment methods Assessment and feedback 2.6 3.0 −0.4 −29.2 −5.5
Feedback Assessment and feedback 5.9 7.3 −1.4 −19.1 −4.1
COVID-19 Others 3.5 3.3 0.2 −18.1 14.8
Organisation & management of course Organisation and management 8.2 3.3 4.8 −16.9 −2.9

Most positive categories (share ≥ 2%)

Category Section Share % Sector % Δ pp Sentiment idx Δ vs sector
General facilities Learning resources 2.2 1.8 0.4 74.0 50.5
Teaching Staff Teaching 9.8 6.7 3.0 56.0 20.5
Student life Learning community 6.9 3.2 3.7 51.1 19.0
Personal development Learning community 2.5 2.5 0.0 43.9 −15.9
Career guidance, support Learning community 2.0 2.4 −0.4 43.7 13.6
Delivery of teaching Teaching 7.7 5.4 2.3 36.9 28.1
Type and breadth of course content Learning opportunities 9.0 6.9 2.1 27.9 5.3

What this means in practice

First, fix the operational rhythm. Publish timetables early, set a clear “change window,” and keep a single source of truth for updates. Name an owner for scheduling and organisation decisions, and push a short weekly “what changed and why” digest. These steps directly target the lowest‑scoring areas (scheduling, organisation, and comms).

Second, make assessment transparent. Use checklist‑style rubrics, short annotated exemplars, and a realistic service level for feedback turnaround. Clarify the purpose of each assessment and how it maps to learning outcomes. This is the fastest way to lift Feedback and Assessment methods.

Third, close the loop on student voice. Show “you said, we did” outcomes and track response times to queries. Preserve and scale what students rate highly—strong teaching practice, supportive culture, and a vibrant learning community—by sharing concrete examples across modules and teams.

Data at a glance (2018–2025)

  • Top topics by share: Teaching Staff (~9.8%), Type and breadth of course content (~9.0%), Organisation & management of course (~8.2%), Delivery of teaching (~7.7%), Scheduling/timetabling (~7.1%), Student life (~6.9%), Student support (~6.8%).
  • Delivery & ops cluster (placements, scheduling, organisation, comms, remote) accounts for ~21.0% of comments and is predominantly negative.
  • People & growth cluster (teaching staff, delivery of teaching, student support, personal tutor, personal development, student life) holds ~36.5% of comments with strongly positive tone.
  • 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 (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 thousands of open-text survey comments into clear, prioritised actions. It tracks topics and sentiment, and shows movement by year, so you can see whether changes improve the right categories across 2018–2025.

It works at whole‑institution scale and for fine‑grained views (faculty, school, department, programme). You get concise, anonymised theme summaries and representative comments for partners and programme teams, without trawling raw text.

Crucially, 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). You can segment by site/provider, cohort and year to target interventions where they will shift sentiment most. Export‑ready outputs (for web, decks and dashboards) make it straightforward to share priorities and progress across the institution.

How to use this subject hub

This page groups Student Voice blog case studies tagged to veterinary medicine and dentistry (CAH3). Use it to see which themes students raise most often in this subject area and what actions tend to follow.

  • Start with the most-read posts to understand the common issues.
  • Use theme links to jump to category hubs (e.g., workload, feedback, teaching).
  • Translate insights into governed evidence via Student Voice Analytics.

Common themes in this subject area (on our blog)

Most-read posts in this subject area

Recommended next steps

  1. Look for repeatability: which themes recur across years and modules?
  2. Check whether issues are structural (resources/staffing) or local (one module/team).
  3. Define what “good” looks like for the subject (examples, rubrics, assessment clarity).
  4. Track movement: do actions reduce volume/negativity for key themes next cycle?

Case studies on teaching, course design and organisation in veterinary studies

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