Student Voice Analytics for Engineering Non-specific — UK student feedback 2018–2025
Scope. UK NSS open‑text comments for Engineering (CAH10-01-01) students across academic years 2018–2025.
Volume. ~3,300 comments; 94.5% successfully categorised to a single primary topic (n=3,117).
Overall mood. Roughly 52.4% Positive, 43.6% Negative, 4.0% Neutral (positive:negative ≈ 1.20:1).
What students are saying
Engineering students centre their feedback on the building blocks of study: learning resources, course content, and assessment. The single largest topic is Learning resources, accounting for around one in ten comments (≈10.7%). Tone here is positive overall (index ~+19.5) and share is far above the sector average for the same topic, though sentiment sits just below sector by a small margin.
Course Content (type and breadth) is also prominent (≈9.5%) and net positive (≈+17.0). By contrast, Module choice/variety features less often but leans negative (≈−6.9) and sits well below sector on tone, signalling some appetite for clearer or more flexible pathways.
Assessment & Feedback is a major thread. Students talk about Feedback in 8.3% of comments with a near‑neutral balance (≈−3.1) but notably better than the wider sector on tone. However, two categories remain clear pain points: Marking criteria (≈−42.8) and Assessment methods (≈−31.1). When expectations, rubrics and exemplars are opaque, sentiment drops sharply.
Operationally, the delivery environment is comparatively steady. Remote learning is relatively common (≈6.8%) and positive (≈+8.5), substantially above sector tone. Organisation and management (≈5.2%) and Scheduling/timetabling (≈4.6%) also trend positive and sit well above sector benchmarks on sentiment, suggesting basic rhythm and predictability are broadly working for this cohort. People‑centred support is a net strength: Teaching Staff are viewed warmly (≈+31.8) and Student support is positive (≈+14.1). Personal development stands out as a highlight (≈+68.0). Placements/fieldwork, by contrast, barely feature in Engineering compared with the sector overall.
Top categories by share (engineering vs sector)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Learning resources | Learning resources | 10.7 | 3.8 | 6.9 | 19.5 | -1.9 |
| Type and breadth of course content | Learning opportunities | 9.5 | 6.9 | 2.6 | 17.0 | -5.6 |
| Feedback | Assessment & feedback | 8.3 | 7.3 | 1.0 | -3.1 | 11.9 |
| Remote learning | The teaching on my course | 6.8 | 3.5 | 3.3 | 8.5 | 17.5 |
| Organisation & management of course | Organisation & management | 5.2 | 3.3 | 1.8 | 14.2 | 28.2 |
| Scheduling/timetabling | Organisation & management | 4.6 | 2.9 | 1.8 | 13.5 | 30.0 |
| Student support | Academic support | 4.1 | 6.2 | -2.1 | 14.1 | 0.9 |
| Teaching Staff | The teaching on my course | 4.0 | 6.7 | -2.7 | 31.8 | -3.7 |
| Personal Tutor | Academic support | 3.9 | 3.2 | 0.7 | 9.0 | -9.7 |
| Delivery of teaching | The teaching on my course | 3.8 | 5.4 | -1.6 | 3.0 | -5.7 |
Most negative categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Marking criteria | Assessment & feedback | 3.7 | 3.5 | 0.1 | -42.8 | 2.9 |
| Workload | Organisation & management | 2.6 | 1.8 | 0.7 | -34.8 | 5.2 |
| Assessment methods | Assessment & feedback | 3.7 | 3.0 | 0.7 | -31.1 | -7.4 |
| COVID-19 | Others | 3.2 | 3.3 | -0.2 | -21.4 | 11.5 |
| Module choice / variety | Learning opportunities | 3.3 | 4.2 | -0.9 | -6.9 | -24.3 |
| IT Facilities | Learning resources | 2.6 | 1.2 | 1.4 | -6.2 | 7.8 |
| Feedback | Assessment & feedback | 8.3 | 7.3 | 1.0 | -3.1 | 11.9 |
Most positive categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| Personal development | Learning community | 3.1 | 2.5 | 0.6 | 68.0 | 8.2 |
| Student life | Learning community | 2.6 | 3.2 | -0.6 | 35.6 | 3.5 |
| Teaching Staff | The teaching on my course | 4.0 | 6.7 | -2.7 | 31.8 | -3.7 |
| Learning resources | Learning resources | 10.7 | 3.8 | 6.9 | 19.5 | -1.9 |
| Type and breadth of course content | Learning opportunities | 9.5 | 6.9 | 2.6 | 17.0 | -5.6 |
| Organisation & management of course | Organisation & management | 5.2 | 3.3 | 1.8 | 14.2 | 28.2 |
| Student support | Academic support | 4.1 | 6.2 | -2.1 | 14.1 | 0.9 |
What this means in practice
- Make assessment clarity non‑negotiable. Publish annotated exemplars, checklist‑style rubrics, and clear marking criteria for every assessment type. Calibrate expectations across markers and modules, and set a realistic service level for feedback so students can act on it.
- Keep the operational rhythm steady. The positive tone around organisation, scheduling and remote learning suggests these foundations are working; protect them with a single source of truth for timetable changes, a weekly “what changed and why” update, and clear ownership for decisions.
- Invest where students feel the benefit daily. Learning resources are a defining theme in Engineering. Maintain availability and reliability, reduce access friction (physical or digital), and signpost how to get the most from what’s provided.
- Use course architecture to reconcile breadth with choice. Where “type and breadth” is valued but “module choice/variety” trends negative, map progression routes, show option trade‑offs early, and ensure outcomes are transparent.
Data at a glance (2018–2025)
- Top topics by share: Learning resources (≈10.7%), Type & breadth of course content (≈9.5%), Feedback (≈8.3%), Remote learning (≈6.8%), Organisation & management of course (≈5.2%).
- Cluster view:
- Delivery & ops (placements, scheduling, organisation, comms, remote): ≈18.6% of all comments, with sentiment above sector for remote learning, organisation and scheduling.
- People & growth (personal tutor, student support, teaching staff, delivery of teaching, personal development, student life): ≈22.8% of comments, strongly positive overall, led by Personal development.
- 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. Sector comparisons refer to like‑for‑like category baselines.
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 by year so you can see what is changing, where, and by how much—at whole‑institution level and for specific schools, departments and programmes.
It also 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 relative to the right peer group. You can segment results by site/provider, cohort and year, and generate concise, anonymised summaries for programme teams and external stakeholders without trawling thousands of responses. Export‑ready outputs (web, deck, dashboard) make it straightforward to share priorities and progress across the institution.
