Student Voice Analytics for Social Sciences — UK student feedback 2018–2025

Scope. UK NSS open-text comments for Social Sciences (CAH15-01-01) students across academic years 2018–2025.
Volume. ~1,682 comments; 96.0% successfully categorised to a single primary topic.
Overall mood. Roughly 54.5% Positive, 41.9% Negative, 3.6% Neutral (positive:negative ≈ 1.30:1).

What students are saying

Students’ comments centre first on the shape and content of the curriculum. “Type and breadth of course content” is the single largest topic by share (~7.9%) and carries a positive tone (index ~+22.2), broadly in line with the sector. Alongside content, there is sustained attention to the mechanics of delivery: “Remote learning” (~7.2%) is widely discussed and leans negative (−10.2), and “Delivery of teaching” (~5.4%) trends slightly negative (−3.8), even as students rate “Teaching Staff” highly (index ~+40.8). In short, the people are praised, while elements of format and experience—especially online—draw more criticism.

Support-focused themes are a clear strength. “Student support” (~7.4%) and “Personal Tutor” (~6.1%) both trend positive and sit above the sector on tone. “Availability of teaching staff” (2.0%) is also warmly viewed. Students also report gains in “Personal development” (3.9%, index ~+55.6), and the wider “Learning resources” offer (6.1%, +26.6) is seen as helpful.

In Assessment & Feedback, patterns split. “Feedback” appears in ~6.6% of comments and is mildly negative (−6.9), though notably less negative than the sector. “Marking criteria” (4.5%) is a clear pain point (−36.9), typically when expectations are unclear. “Assessment methods” (2.8%) also leans negative (−20.7). Clarity and exemplars remain the quickest route to improvement.

Operational themes are mixed but comparatively lighter in volume for this discipline. “Organisation and management of course” (3.4%) sits near neutral (−0.1) and above the sector benchmark on tone, while “Scheduling/timetabling” (2.9%) is positive (+16.8) and well above sector. “Communication about course and teaching” appears less often (1.5%) but is sharply negative (−39.8). Notably, “Placements/fieldwork” barely feature (0.1% vs sector 3.4%), indicating that practice-based logistics are not a dominant part of the student experience in this area.

Top categories by share (Social Sciences vs sector)

Most negative categories (share ≥ 2%)

Most positive categories (share ≥ 2%)

What this means in practice

• Make assessment expectations unmissable. Where “Marking criteria” and “Assessment methods” drive negativity, publish annotated exemplars, checklist-style rubrics, and plain‑English marking guides. Agree and communicate realistic turnaround times, then meet them.

• Stabilise online learning journeys. For “Remote learning” and “Delivery of teaching”, set a single source of truth for materials and updates; use consistent layouts; provide short “what to do this week” summaries; and capture recordings with clear audio and slides.

• Keep investing in people-centred support. Protect the responsiveness of personal tutors and the broader support offer—students notice and value it. Use these touchpoints to signpost assessment support and study resources.

• Tighten delivery comms. Even with relatively positive scheduling and organisation, the subset of comments on course communications remains negative. A weekly digest (“what changed and why”) and named ownership for updates reduce confusion quickly.

Data at a glance (2018–2025)

• Top topics by share: Type & breadth of course content (≈7.9%), Student support (≈7.4%), Remote learning (≈7.2%), Feedback (≈6.6%), Learning resources (≈6.1%), Personal Tutor (≈6.1%), Delivery of teaching (≈5.4%).
• Cluster view:
  • Delivery & ops (remote learning, scheduling, organisation, comms, placements): ~15.1% of all comments.
  • People & growth (student support, personal tutor, teaching staff, availability of staff, delivery of teaching, personal development, student life): ~33.0%.
  • Assessment & feedback (feedback, marking criteria, assessment methods, dissertation): ~15.0%.
• Topics under- or over-discussed vs sector: placements/fieldwork appear far less (0.1% vs 3.4%), while learning resources and support topics appear more often than sector averages.
• 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), averaged at category level.

How Student Voice Analytics helps you

Student Voice Analytics turns open-text survey responses into clear, defensible priorities. It tracks topics and sentiment over time so programme, department and school teams can see which categories move, why, and where to act—across the whole institution or within a specific course.

The platform 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 progress relative to the right peer group. You can segment by site/provider, cohort, and year to pinpoint where interventions will have most impact. Concise, anonymised summaries and representative comments make it easy to brief partners and programme teams, and export‑ready outputs (web, deck, dashboard) support straightforward sharing of priorities and progress.

How to use this subject hub

This page groups Student Voice blog case studies tagged to social sciences (non-specific) (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)

• Remote Learning
• Teaching Staff

Most-read posts in this subject area

• What are students saying about teaching staff in social sciences?
• Should social science students embrace remote learning?

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?