Student Voice Analytics for Literature in English — UK student feedback 2018–2025
Scope. UK NSS open-text comments for Literature in English (CAH19-01-03) students across academic years 2018–2025.
Volume. The current extract contains no category-level records, so volumes and coverage cannot be reported.
Overall mood. Sentiment distribution is not available in this extract.
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.
Insights into specific areas of english literature education
