What are students actually saying about Module choice variety (NSS 2018–2025)?

Students are broadly positive about the choice and variety of modules available, but notable gaps appear for mature and part‑time learners, and in several technical subject areas.

Scope: UK NSS open‑text comments tagged to Module choice variety across academic years 2018–2025.
Volume: ~15,673 comments within this category (from 385,317 total); 100% had sentiment classified.
Overall mood: 64.6% Positive, 31.8% Negative, 3.6% Neutral (sentiment index +27.8; positive:negative ≈ 2.0:1).

What students are saying in this category

The tone is clearly positive overall, suggesting students value choice when it is well signposted and practically accessible. Most commentary comes from young (84%) and full‑time (86%) students, who report stronger sentiment (+30.4 and +30.2 respectively).

By contrast, mature (+14.5) and part‑time (+12.3) learners are markedly less positive, pointing to practical constraints (timetabling, capacity, or eligibility rules) that make optionality harder to realise for these cohorts. Females (+31.8) are more positive than males (+22.4). There is only a small gap between disabled (+26.5) and non‑disabled (+28.3) students.

Subject patterns vary: language/humanities areas sit at the top of the distribution, while computing and engineering cohorts are least positive about module choice. This likely reflects differences in capacity, prerequisites, or compulsory components limiting optional routes.

Trend & benchmarks

Selected segment snapshot

CAH subject areas: most vs least positive (n ≥ 300)

What this means in practice

• Publish the full module diet early with clear prerequisites, caps and known clashes; label “high‑demand” options and provide viable fallbacks.
• Run capacity and clash checks before enrolment windows open; aim for a “no‑clash” timetable for the most common option pairs.
• Operate transparent, fair allocation: visible waiting lists, time‑stamped queues, and rules for priority (e.g., finalists, prerequisites).
• Improve inclusivity for mature and part‑time students: offer flexible slots, evening/online variants where feasible, and avoid single‑slot bottlenecks.
• Provide a short, low‑friction switching window after teaching starts, with clear deadlines and academic advice embedded.
• Monitor equity by cohort and subject: track sentiment and fill rates by mode, age and CAH area; intervene where indices lag (e.g., computing/engineering).
• Close the loop: publish a concise “what changed and why” after allocation cycles, including added capacity or redesigned options.

How Student Voice Analytics helps you

• Surfaces topic and sentiment over time for Module choice variety, with drill‑downs from provider to school/department and cohort.
• Like‑for‑like comparisons across CAH subject areas and demographics (e.g., age, domicile, mode, campus/site, commuter status).
• Flags cohorts at risk (e.g., mature/part‑time) and subject clusters with persistent constraints.
• Export‑ready tables and concise summaries for programme boards and timetabling/resource planning meetings.

Data at a glance (2018–2025)

• Volume: ~15,673 comments on Module choice variety (from 385,317 total).
• Coverage: 100.0% of category comments had sentiment classified.
• Overall mood: 64.6% Positive, 31.8% Negative, 3.6% Neutral (index +27.8; ≈2.0:1 positive:negative).