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%).
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