What UK Students Say About Personal Development: NSS Feedback Analysis (9,219 Comments, 2018–2025)

Students are overwhelmingly positive about how their courses support personal development. Tone is consistently strong across major student groups, with small gaps by disability, mode and sex.

Key findings

9,219 comments analysed across UK programmes (2018–2025)
The signal is strongly positive across the board. Young and mature students are near-identical on tone (indices 69.1 vs 68.4).
Small gaps to watch: disabled students (66.3) trail those not disabled (69.5); part-time (67.4) sit below full-time (69.2); male students (66.4) are below fe...

What students are saying in this category

The signal is strongly positive across the board. Young and mature students are near-identical on tone (indices 69.1 vs 68.4).
Small gaps to watch: disabled students (66.3) trail those not disabled (69.5); part-time (67.4) sit below full-time (69.2); male students (66.4) are below female students (70.3).
By subject mix (CAH), larger groups mostly cluster in the high 60s to low 70s. Subjects allied to medicine (73.0) and business and management (71.4) lead; design/creative (65.1) and computing (66.2) are relatively lower but still positive.
Comment volume in this category skews towards full-time (70.5%) and younger students (63.6%); 60.5% of comments are from female students.

Segment variation at a glance

Segment	Comments	Positive %	Negative %	Sentiment idx
Age — Young	5,866	90.5	8.6	69.1
Age — Mature	3,145	91.0	8.0	68.4
Disability — Not disabled	7,098	91.1	8.0	69.5
Disability — Disabled	1,912	89.2	9.9	66.3
Mode — Full-time	6,501	90.7	8.4	69.2
Mode — Part-time	2,421	90.3	8.6	67.4
Mode — Apprenticeship	78	100.0	0.0	84.7
Sex — Female	5,582	91.3	7.8	70.3
Sex — Male	3,411	89.6	9.4	66.4

Note: Sentiment index ranges −100 to +100; higher is more positive. Apprenticeship volumes are small.

Subject mix within this category (CAH, larger samples ≥300)

Subject group (CAH)	Comments	Sentiment idx
Subjects allied to medicine (CAH02)	990	73.0
Business and management (CAH17)	565	71.4
Psychology (CAH04)	580	69.9
Language and area studies (CAH19)	380	69.9
Social sciences (CAH15)	861	68.7
Historical, philosophical and religious studies (CAH20)	395	67.9
Engineering and technology (CAH10)	331	67.2
Biological and sport sciences (CAH03)	328	66.9
Combined and general studies (CAH23)	405	66.3
Computing (CAH11)	428	66.2
Design, and creative and performing arts (CAH25)	636	65.1

What this means in practice

Protect and scale what works: keep personal growth activities visible, timely and clearly linked to outcomes (confidence, skills, next steps).
Close small gaps for disabled and part-time students: check access to development opportunities (timing, format, location, accessibility), monitor participation, and follow up with targeted nudges.
Address the male–female gap: ensure development activities feel relevant and inclusive, and showcase diverse role models and pathways.
Prioritise support in relatively lower-index subject areas (e.g., design/creative, computing, engineering): embed structured reflection, authentic projects and clear progression routes.

How Student Voice Analytics helps you

Track topic tone and volume over time, with drill-down from institution to school/department and cohort.
Like-for-like comparisons across CAH subject groups and by demographics (age, disability, ethnicity, mode, sex).
Easy export of concise, anonymised summaries for programme teams and committees.

Data at a glance (2018–2025)

Volume: 9,219 comments; 100.0% sentiment coverage.
Overall mood: 90.3% Positive, 8.8% Negative, 0.9% Neutral; sentiment index +68.2.
Who’s speaking: 63.6% young; 70.5% full-time; 60.5% female.

How to use this data

This page presents sector-level student feedback analysis for the Personal Development category (Learning community), with demographic and subject-area benchmarks you can reference directly in institutional documents.

Use this for

Annual Programme Review (APR) — reference the segment benchmarks to contextualise your programme's feedback patterns against the sector.
TEF and quality enhancement — cite the demographic breakdowns and subject-area sentiment as evidence of awareness of differential student experience.
Equality, diversity and inclusion (EDI) — use the ethnicity, disability and age segment data to evidence where feedback experience differs by student group.
Staff-Student Liaison Committees (SSLCs) — share the key findings and subject-area table as discussion starters with student representatives.
Action planning — use the "What this means in practice" recommendations as a starting point for targeted interventions.