Student Voice Analytics for Mathematics — UK student feedback 2018–2025

Scope. UK NSS open‑text comments for Mathematics (CAH09-01-01) students across academic years 2018–2025.
Volume. ~7,159 comments; 96.7% successfully categorised to a single primary topic.
Overall mood. Roughly 51.7% Positive, 44.7% Negative, 3.7% Neutral (positive:negative ≈ 1.16:1).

What students are saying

Mathematics students talk most about the basics that make study workable day to day. The largest single topic is Learning resources (8.3% share), discussed more than in the sector overall (+4.5 pp). The tone is clearly positive (index +23.8 and slightly above sector). Within this space, students praise access to resources (e.g., libraries and study spaces trend positive), while IT facilities are a known weak spot (index −25.2).

People‑centred themes are prominent and positive. Teaching Staff attract warm sentiment (index +32.9) and account for 7.4% of all comments. Availability of teaching staff is a standout strength (+44.1), and Personal Tutor is also clearly positive (+23.1). Student life trends positive too (+25.5).

By contrast, Assessment & Feedback is the core friction. Feedback (7.0% share) is negative overall (−11.0), though slightly better than sector. Assessment methods (−36.4) and Marking criteria (−43.2) drive stronger dissatisfaction, pointing to a need for clearer task design, transparent criteria, and alignment between teaching and assessment.

Course delivery and operations are mixed. Delivery of teaching sits close to neutral but below sector (−1.6; Δ vs sector −10.4). Scheduling/timetabling (−18.8) and Workload (−46.5) are persistent pain points. Organisation and management of the course is less negative than sector on average (−2.5; Δ +11.5), yet students still call for predictability and coordination. Remote learning remains a net negative (−11.5). As expected for this discipline, Placements/fieldwork are scarcely mentioned (0.1% vs sector 3.4).

Curriculum structure lands well. Type and breadth of course content (6.8%) and Module choice/variety (6.5%) are both positive (around +18 to +21), with module choice notably over‑indexed vs sector (+2.3 pp). Student support as a general category registers near neutral (+0.6) and sits below sector on tone, suggesting room to standardise quality across cohorts.

Top categories by share (Mathematics vs sector)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Learning resources	Learning resources	8.3	3.8	4.5	23.8	2.4
Teaching Staff	The teaching on my course	7.4	6.7	0.6	32.9	−2.7
Feedback	Assessment & feedback	7.0	7.3	−0.4	−11.0	4.0
Type & breadth of course content	Learning opportunities	6.8	6.9	−0.1	20.5	−2.0
Module choice / variety	Learning opportunities	6.5	4.2	2.3	18.3	0.9
Delivery of teaching	The teaching on my course	6.3	5.4	0.8	−1.6	−10.4
Student support	Academic support	5.5	6.2	−0.7	0.6	−12.6
Assessment methods	Assessment & feedback	4.4	3.0	1.4	−36.4	−12.6
Remote learning	The teaching on my course	4.1	3.5	0.6	−11.5	−2.5
Student life	Learning community	3.1	3.2	−0.1	25.5	−6.6

Most negative categories (share ≥ 2%)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Workload	Organisation & management	2.4	1.8	0.6	−46.5	−6.5
Marking criteria	Assessment & feedback	2.6	3.5	−1.0	−43.2	2.5
COVID-19	Others	2.9	3.3	−0.4	−41.6	−8.7
Assessment methods	Assessment & feedback	4.4	3.0	1.4	−36.4	−12.6
Scheduling/timetabling	Organisation & management	3.0	2.9	0.1	−18.8	−2.3
Remote learning	The teaching on my course	4.1	3.5	0.6	−11.5	−2.5
Feedback	Assessment & feedback	7.0	7.3	−0.4	−11.0	4.0

Most positive categories (share ≥ 2%)

Category	Section	Share %	Sector %	Δ pp	Sentiment idx	Δ vs sector
Availability of teaching staff	Academic support	2.3	2.1	0.2	44.1	4.8
Teaching Staff	The teaching on my course	7.4	6.7	0.6	32.9	−2.7
Student life	Learning community	3.1	3.2	−0.1	25.5	−6.6
Learning resources	Learning resources	8.3	3.8	4.5	23.8	2.4
Personal Tutor	Academic support	3.1	3.2	−0.1	23.1	4.4
Type & breadth of course	Learning opportunities	6.8	6.9	−0.1	20.5	−2.0
Module choice / variety	Learning opportunities	6.5	4.2	2.3	18.3	0.9

What this means in practice

Clarify assessment design. Publish concise rubrics with annotated exemplars; align each assessment method with the taught content and its learning outcomes; and set realistic, visible service levels for feedback. Tackling Assessment methods, Marking criteria and Feedback together will reduce the largest sources of assessment‑related dissatisfaction.
Stabilise the delivery rhythm. Name an owner for scheduling, keep a single source of truth for course communications, and provide a brief weekly update (“what changed and why”). These moves lift Delivery of teaching, Scheduling and Organisation/management simultaneously.
Protect and extend the resource strengths. Keep the good experience around libraries and study spaces visible; address the IT Facilities gap with basic reliability fixes and quick‑win support (e.g., device checks, software access guides).
Manage workload explicitly. Map weekly effort to credits, avoid assessment bunching, and signal peak weeks early. Where you cannot reduce load, improve sequencing and transparency.

Data at a glance (2018–2025)

Top topics by share are stable: Learning resources (≈8.3%), Teaching Staff (≈7.4%), Feedback (≈7.0%), Type & breadth of course content (≈6.8%), Module choice/variety (≈6.5%), Delivery of teaching (≈6.3%).
Clusters: the delivery & ops cluster (placements/fieldwork, scheduling, organisation & management, course communications, remote learning, workload) accounts for ≈13.8% of all comments; the people & growth cluster (personal tutor, student support, teaching staff, delivery of teaching, availability of staff, personal development, student life) accounts for ≈29.4%.
Topics under‑represented vs sector: Placements/fieldwork (0.1% vs 3.4%) and Year abroad (0.1% vs 0.6%), indicating the conversation here focuses more on curriculum, teaching and assessment than on fieldwork logistics.

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 open‑text survey responses into clear priorities. It tracks topics and sentiment over time, so you can see which categories are moving, by how much, and where to act first—at whole‑institution level and down to schools and departments.

It also enables like‑for‑like sector comparisons across CAH codes and demographics, so you can evidence improvement against the right peer group, not just the whole sector. You can segment by site/provider, cohort and year of study, and produce concise, anonymised summaries for programme teams and external stakeholders. Export‑ready outputs (web, deck, dashboard) make it straightforward to share priorities and progress across your institution.