Student Voice Analytics for Medical Technology — UK student feedback 2018–2025

Scope. UK NSS open‑text comments for Medical Technology (CAH02-05-01) across academic years 2018–2025.
Volume. ~2,033 comments; 98% successfully categorised to a single primary topic.
Overall mood. Roughly 53.5% Positive, 42.1% Negative, 4.4% Neutral (positive:negative ≈ 1.27:1).

What students are saying

Medical Technology students consistently talk about real‑world learning, operations and people. The largest single topic is Placements/fieldwork/trips: around one in five comments (≈19.9%) focus here. The tone is positive (sentiment index ~+14.4) and sits modestly above the sector baseline for this topic. What drives that tone is the perceived value of applied experience when logistics and support work smoothly.

A sizeable operations cluster — scheduling/timetabling, organisation and management, course communications, and remote learning — plus placements accounts for about a third of all comments (≈34.7%). Within it, Scheduling is the outlier on tone (index ~−29.0, notably below sector), and Communication about course and teaching also leans negative. Organisation is slightly negative but a little better than sector; Remote learning is near neutral and better than sector. The pattern is simple: students want predictability, timely updates and a clear “source of truth” when plans change.

Set against this, people‑centred support is a clear strength. Student support (index ~+35.7, well above sector), Teaching Staff (+33.1), Availability of teaching staff (+36.8) and Delivery of teaching (+18.7) are warmly received. Students also report strong Personal development (+74.0) and positive Student life (+39.0), albeit these appear in smaller volumes of comments.

In Assessment & feedback, the friction points mirror the sector’s. Feedback appears in ~4.7% of comments with a negative tone (−18.8); Marking criteria is strongly negative (−42.0), and Assessment methods also lean negative (−23.4). The recurring theme is clarity: students respond better when criteria are explicit, exemplars are available and turnaround expectations are reliable.

Finally, some topics are under‑discussed relative to sector — Module choice/variety and Learning resources, for example — while Student voice appears more often than the sector average but still carries a mildly negative tone. This distribution emphasises that delivery mechanics and applied learning dominate students’ day‑to‑day experience.

Top categories by share (Medical Technology vs sector):

Most negative categories (share ≥ 2%)

Shares are the proportion of all Medical Technology 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%)

What this means in practice

• Treat placements and fieldwork as a designed service. Keep capacity planning tight, set clear expectations with hosts, and ensure structured on‑site support. Capture “what worked/what to change” after each placement cycle to sustain the positive tone students already report.

• Reduce operational noise. Publish a single, authoritative timetable; minimise last‑minute changes; and issue a short weekly “what changed and why” update. Name owners for scheduling and course communications so students know where to look and who is accountable.

• Make assessment transparency a habit. Share checklist‑style rubrics and annotated exemplars; clarify how criteria map to grades; and set a realistic feedback SLA that is monitored and reported back to students. These simple moves typically lift Feedback and Marking criteria sentiment quickly.

• Close the loop on the student voice. Because students are talking about the “Student voice” category more than the sector average, routinely show “you said, we did” outcomes and track response times to demonstrate that feedback prompts action.

Data at a glance (2018–2025)

• Top topics by share: Placements/fieldwork/trips (≈19.9%), Student support (≈7.2%), Teaching Staff (≈6.7%), Delivery of teaching (≈4.9%), Feedback (≈4.7%).
• Delivery & ops cluster (placements, scheduling, organisation, course communications, remote learning): ≈34.7% of all comments. Tone is mixed — placements positive; scheduling and comms negative; organisation slightly negative; remote learning near neutral.
• People & growth cluster (personal tutor, student support, teaching staff, availability of teaching staff, delivery of teaching, personal development, student life): ≈26.8% of all comments with strongly positive tone across most categories.
• 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 and sentiment over time, across the whole institution and at fine‑grained levels (school, department, programme), so teams can focus on high‑impact areas like Placements, Scheduling, Organisation, Communications and Assessment.

It also lets you prove change on a like‑for‑like basis, with 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 move sentiment most. Concise, anonymised theme summaries and representative comments make it easy to brief partners and programme teams, and export‑ready outputs (for web, slide deck or dashboard) support sharing priorities and progress across the institution.