What UK English Literature Students Say: NSS Feedback Analysis (2018–2025)
What students are saying
This extract does not include topic-level rows (categories with shares and sentiment indices), so we cannot summarise the main patterns for Literature in English at this time.
When the dataset is populated, this section will highlight:
- Which topics dominate student comments (share of voice),
- The tone students use for each topic (sentiment index),
- Where this discipline differs from the sector baseline on both volume and sentiment.
Top categories by share (discipline vs sector):
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No data available | — | — | — | — | — | — |
Most negative categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No data available | — | — | — | — | — | — |
Most positive categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No data available | — | — | — | — | — | — |
What this means in practice
- Prioritise by volume × tone × gap. When data are available, focus first on categories that combine high share, negative sentiment, and a negative gap vs sector. These are the most visible pain points for students.
- Strengthen the operational rhythm. Clear ownership for timetable and course communications, a single source of truth for updates, and predictable change windows reduce frustration and stabilise sentiment.
- Make assessment expectations transparent. Publish annotated exemplars, checklist-style rubrics, and reliable feedback turnaround times. Clarity reduces anxiety and moves sentiment fastest in assessment-related categories.
- Invest in people-centred support. Visible, proactive personal tutoring and accessible teaching staff tend to lift tone across multiple areas when present and well-signposted.
Data at a glance (2018–2025)
- The current extract includes no category-level records, so top topics by share and cluster summaries (e.g., delivery/operations vs people/growth) cannot be computed.
- How to read the numbers (when available). 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 columns show like-for-like comparisons for the same categories.
How Student Voice Analytics helps you
Student Voice Analytics turns open-text survey comments into clear, prioritised actions by tracking topics, sentiment and movement by year for any CAH code, including Literature in English. It supports whole‑institution views as well as fine‑grained analysis at faculty, school and programme level, with concise, anonymised summaries that are easy to brief to programme teams and external stakeholders.
Most importantly, it enables like‑for‑like proof of change. You can run sector comparisons across CAH codes and by demographics (e.g., year of study, domicile, mode of study, campus/site, commuter status) to see whether this discipline is improving relative to the right peer group. Flexible segmentation (site/provider, cohort, year) and export‑ready outputs (web, deck, dashboard) make it straightforward to share priorities and progress across your organisation.
How to use this data
This page presents sector-level student feedback analysis for English Literature, with sentiment benchmarks and topic breakdowns you can reference directly in institutional documents.
Use this for
- Annual Programme Review (APR) — reference the top-categories table and sentiment benchmarks to contextualise your programme's results against the discipline.
- TEF and quality enhancement — cite the sentiment index and sector delta columns as evidence of awareness of student priorities relative to the sector.
- Professional body revalidation — draw on placement, assessment and support data for evidence of responsiveness to student feedback in your discipline.
- Staff-Student Liaison Committees (SSLCs) — share the key findings and most-negative categories as discussion starters with student representatives.
- New programme design — use the topic share and sentiment data to anticipate which aspects of the student experience will need proactive attention.
Common themes in this subject area (on our blog)
- Remote Learning
- Dissertation
- COVID-19
- Strike Action
- Feedback
- Student Life
- Assessment Methods
- Opportunities to Work with Other Students
Most-read posts in this subject area
- Do peer opportunities improve learning for literature students?
- What does student life look like for literature in English students?
- What does effective feedback look like in English Literature programmes?
- Do strikes disrupt learning and creativity on English Literature courses?
- How did COVID-19 change English literature students’ experience?
- Is English Literature course content broad enough?
- What support do universities provide for literature students?
- Does remote learning work for English literature students?
- Are assessment methods holding back literature in English students?
- What helps English literature students succeed in the dissertation?
Recommended next steps
- Look for repeatability: which themes recur across years and modules?
- Check whether issues are structural (resources/staffing) or local (one module/team).
- Define what “good” looks like for the subject (examples, rubrics, assessment clarity).
- Track movement: do actions reduce volume/negativity for key themes next cycle?
Cite this page
Student Voice AI (2025). "English Literature student feedback analysis (CAH19-01-03)." Student Voice AI. https://www.studentvoice.ai/cah3/literature-in-english/
Case studies on assessment, module choice and teaching in English literature
