What are students actually saying about Type and breadth of course content (NSS 2018–2025)?

Students are broadly positive about the scope and variety of what they study. Tone is consistently upbeat across most groups, with stronger positivity among mature, part-time and female students. A small apprenticeship cohort is notably less positive, pointing to alignment and relevance checks in work‑based routes. Across subjects, humanities and psychology lean particularly positive, while fast‑moving, applied areas show more mixed views.

Scope: UK NSS open-text comments tagged “Type and breadth of course content” across academic years 2018–2025.
Volume: 25,847 comments within this category (≈6.7% of all 385,317 comments); 100.0% sentiment coverage.
Overall mood: 70.6% Positive, 26.2% Negative, 3.3% Neutral (positive:negative ≈ 2.7:1); sentiment index +39.8.

What students are saying in this category

The overall tone is positive across the board. Most comments reflect appreciation for the variety and scope of course content rather than calls for wholesale change.
Mature (+41.8) and part-time (+43.0) students are slightly more positive than the large full‑time and young cohorts (both ≈+39 to +40), suggesting breadth may better match varied prior experience and time constraints when delivery is flexible.
Apprenticeship students (small base, n=145) are less positive (+24.3), indicating opportunities to tighten the fit between on‑the‑job reality and taught content.
By subject group (CAH1), historical/philosophical/religious studies (+52.4) and psychology (+50.1) stand out for strong positivity, while computing (+28.5) and media/journalism (+27.1; smaller base) are more reserved—typical of fast‑changing fields where currency and applied variety matter.

Snapshot by segment

Selected cohorts (share within this category)

Segment (selected)	Share %	N	Sentiment idx	Positive %	Negative %
Age — Young	68.5	17,718	39.4	70.6	26.0
Age — Mature	29.4	7,599	41.8	71.1	26.0
Mode — Full-time	72.3	18,696	39.2	70.6	26.0
Mode — Part-time	24.9	6,445	43.0	71.5	25.8
Mode — Apprenticeship	0.6	145	24.3	60.7	33.1
Sex — Female	52.3	13,529	43.2	72.5	24.5
Sex — Male	45.5	11,759	36.5	68.6	27.7
Disability — Disabled	18.4	4,762	42.3	72.0	24.6
Disability — Not disabled	79.6	20,562	39.6	70.4	26.3

Top subject areas (CAH1), by volume within this category

Subject group (CAH1)	Share %	N	Sentiment idx	Positive %	Negative %
Social sciences (CAH15)	9.8	2,527	42.5	71.9	24.9
Subjects allied to medicine (CAH02)	7.6	1,963	40.9	71.6	25.1
Business and management (CAH17)	6.7	1,730	34.0	67.1	29.1
Psychology (CAH04)	6.3	1,628	50.1	76.2	21.4
Computing (CAH11)	5.8	1,490	28.5	62.3	34.2
Engineering and technology (CAH10)	5.3	1,379	34.0	68.9	28.2
Combined and general studies (CAH23)	4.8	1,239	45.0	72.3	24.3
Biological and sport sciences (CAH03)	4.3	1,118	44.2	75.1	21.7
Historical, philosophical and religious studies (CAH20)	4.2	1,086	52.4	77.8	19.2
Language and area studies (CAH19)	3.5	917	41.4	71.4	26.7

Notes on numbers: The sentiment index runs from −100 to +100 and reflects the balance of positive vs negative statements.

What this means in practice

Make the content map visible: publish a one‑page “breadth map” showing how core and optional topics build across years and where students can personalise depth.
Protect real choice: schedule options to avoid clashes; guarantee a minimum number of viable option pathways per cohort.
Keep content current: introduce a lightweight quarterly refresh for readings, datasets, case studies and tools—especially in fast‑moving areas.
Balance theory and application: ensure each term includes varied formats (e.g., case, lab/studio, project, seminar) to demonstrate breadth in practice.
Close duplication/gap loops: run an annual content audit with quick wins tracked to closure; ask students to flag “missing or repeated” topics in week 4 and week 9 pulse checks.
Support flexible learners: provide equivalent asynchronous materials and clear signposting so part‑time learners can access the same breadth.
Align work‑based routes: co‑design with employers to map on‑the‑job tasks to module outcomes; set a cadence for updating examples to match workplace realities.

How Student Voice Analytics helps you

See category movement over time and by segment (demographics, mode, site/provider, cohort) with exportable summaries for programme and module teams.
Drill from institution to school/department and subject group (CAH levels), and compare like‑for‑like peer clusters by CAH code and demographics.
Generate concise, anonymised briefs showing what changed, for whom, and where to act next—ready for Boards of Study, APRs and student‑staff committees.

Data at a glance (2018–2025)

Volume: 25,847 comments (100.0% classified to sentiment).
Share of all NSS comments: 6.7%.
Overall mood: 70.6% Positive, 26.2% Negative, 3.3% Neutral (index +39.8; ≈2.7:1 positive:negative).