Student Voice Analytics for English Studies Non-specific — UK student feedback 2018–2025
Scope. UK NSS open-text comments for English Studies (CAH19-01-01) students across academic years 2018–2025.
Volume. Category-level metrics are not available in this extract; no classified comments were provided.
Overall mood. Not available in this extract.
What students are saying
This extract does not include any category rows, so we cannot summarise specific topics or sentiment for English Studies at this time. When the underlying data is available, the analysis will identify which categories dominate student comments, how their tone trends over time, and where the discipline differs most from sector patterns.
Top categories by share (discipline vs sector):
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No category data available in this extract | — | — | — | — | — | — |
Most negative categories (share ≥ 2%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No categories meet the threshold in this extract | — | — | — | — | — | — |
Shares are the proportion of all English Studies 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%)
| Category | Section | Share % | Sector % | Δ pp | Sentiment idx | Δ vs sector |
|---|---|---|---|---|---|---|
| No categories meet the threshold in this extract | — | — | — | — | — | — |
What this means in practice
- Prioritise the categories with the largest share once data is available; these are the themes most students experience day to day.
- Focus early improvement on categories with low sentiment indices and clear operational levers (e.g., timetabling reliability, transparency of communications, clarity of assessment criteria and feedback turnaround).
- Protect strengths by identifying categories with high sentiment and meaningful share (e.g., staff support and delivery elements) and ensuring practices there are documented and replicated.
Data at a glance (2018–2025)
- No category rows were provided in this extract, so top topics, sector comparisons, cluster shares, and sentiment splits cannot be displayed.
- 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 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 free‑text survey responses into clear, prioritised actions by tracking topics and sentiment over time for every discipline and school. It supports whole‑institution views as well as fine‑grained department and programme analyses, with concise anonymised summaries for partners and programme teams.
Critically, it 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 improvement against the right peer group. You can also segment by site/provider, cohort and year to target interventions precisely. Export‑ready outputs (web, deck, dashboard) make it straightforward to share priorities and progress across the institution.
Insights into specific areas of english studies (non-specific) education
