What are students actually saying about Assessment methods (NSS 2018–2025)?
Students’ comments on how they are assessed lean clearly negative overall. Mature and part-time students are more critical than their peers, and tone varies noticeably by subject area, with some STEM disciplines among the most negative.
Scope: UK NSS open-text comments tagged to Assessment methods across academic years 2018–2025.
Volume: 11,318 comments (≈2.9% of all NSS comments); 100.0% scored for sentiment.
Overall mood: 28.0% Positive, 66.2% Negative, 5.8% Neutral (positive:negative ≈ 0.42:1).
Sentiment index: −18.8 (−100 to +100, where negative values mean more negative than positive).
What students are saying in this category
- The tone is consistently critical (−18.8), with over two in three sentiment-bearing sentences negative. This pattern holds across most segments.
- Mature students (−23.9) and part-time learners (−24.6) are notably more negative than young (−17.4) and full-time students (−17.4).
- Disabled students are more negative (−22.1) than those not disabled (−18.2).
- Not UK domiciled students are markedly more negative (−25.1) than White students (−17.5).
- Subject differences are material. Among higher-volume areas, Computing (−24.5), Engineering (−25.5), and Medicine & Dentistry (−28.0) stand out as more negative; Geography/Earth/Environmental studies (−6.5) and Media/Journalism/Communications (−3.3) are less negative. Mathematical Sciences (−31.7) is a notable low point.
Benchmarks across segments
Top subject groups by volume (CAH1)
| Subject group (CAH1) |
n |
Share % |
Sentiment idx |
| (CAH15) social sciences |
1,043 |
9.2 |
−18.7 |
| (CAH02) subjects allied to medicine |
975 |
8.6 |
−18.1 |
| (CAH17) business and management |
903 |
8.0 |
−13.8 |
| (CAH04) psychology |
796 |
7.0 |
−19.9 |
| (CAH03) biological and sport sciences |
644 |
5.7 |
−13.0 |
| (CAH11) computing |
591 |
5.2 |
−24.5 |
| (CAH10) engineering and technology |
578 |
5.1 |
−25.5 |
| (CAH01) medicine and dentistry |
504 |
4.5 |
−28.0 |
| (CAH16) law |
494 |
4.4 |
−14.6 |
| (CAH20) historical, philosophical and religious studies |
407 |
3.6 |
−15.4 |
Key demographic contrasts
| Segment |
n |
Positive % |
Negative % |
Sentiment idx |
| Age: Young |
8,617 |
29.0 |
64.9 |
−17.4 |
| Age: Mature |
2,494 |
23.5 |
71.3 |
−23.9 |
| Mode: Full-time |
8,892 |
29.0 |
64.9 |
−17.4 |
| Mode: Part-time |
2,133 |
23.3 |
72.0 |
−24.6 |
| Disability: Not disabled |
9,144 |
28.1 |
65.8 |
−18.2 |
| Disability: Disabled |
1,969 |
26.3 |
68.8 |
−22.1 |
| Ethnicity: White |
7,308 |
29.1 |
65.7 |
−17.5 |
| Ethnicity: Not UK domiciled |
1,166 |
22.2 |
72.0 |
−25.1 |
| Ethnicity: Asian |
1,302 |
25.7 |
65.7 |
−20.8 |
| Ethnicity: Black |
419 |
29.8 |
61.8 |
−15.5 |
| Sex: Female |
6,445 |
28.5 |
65.8 |
−18.1 |
| Sex: Male |
4,654 |
26.8 |
67.1 |
−19.9 |
Notes: Very small cells (e.g., n < 100) should be treated with caution.
What this means in practice
With the category skewing negative across the board—and especially for mature, part-time, disabled and not UK domiciled students—prioritise clarity, parity and flexibility in how assessments are designed and communicated.
- Make the method unambiguous
- Publish a one-page “assessment method brief” per task: purpose, how it will be marked, weighting, allowed resources, and common pitfalls.
- Use checklist-style rubrics with clearly separated criteria and grade descriptors.
- Calibrate for consistency
- Run quick marker calibration using 2–3 anonymised exemplars at grade boundaries; record moderation notes.
- For larger cohorts, sample double-marking with targeted spot checks where variance is highest.
- Reduce friction for diverse cohorts
- For mature/part-time learners, offer predictable submission windows, early release of briefs, and asynchronous alternatives for oral components.
- For not UK domiciled students, provide short orientation on assessment formats, academic integrity, and referencing conventions with mini-practice tasks.
- Build accessibility in from the start: alternative formats, captioned/oral options, and plain-language instructions.
- Coordinate at programme level
- Publish a single assessment calendar to avoid deadline pile-ups and method clashes across modules.
- Avoid duplication of methods within the same term; aim for a balanced mix aligned to learning outcomes.
- Close the loop
- Provide a brief post-assessment debrief summarising common strengths and issues (even before individual marks) to improve perceived fairness and transparency.
How Student Voice Analytics helps you
- Cuts your data by discipline (CAH), demographics (age, mode, domicile/ethnicity, disability), and cohort/site to pinpoint where assessment method issues concentrate.
- Tracks sentiment for this category over time and surfaces concise, anonymised summaries you can share with programme and module teams.
- Supports like-for-like comparisons by subject mix and cohort profile, with export-ready tables for boards and quality reviews.
Data at a glance (2018–2025)
- Volume: 11,318 comments tagged to Assessment methods; 100.0% sentiment coverage.
- Overall mood: 28.0% Positive, 66.2% Negative, 5.8% Neutral (index −18.8).
- Largest segments by volume: Young (76.1%), Full-time (78.6%), White (64.6%).