Student Voice Analytics

Answer first

Context

Universities capture thousands of free‑text comments each year. Without HE‑specific methods, most never become decision‑grade evidence. Student Voice Analytics fixes that with UK‑HE–tuned categorisation, all‑comment coverage, and sector benchmarking designed for Student Experience and Market Insights across UG, PGT, PGR and Welcome & Belonging surveys. For survey context and timing, see the official Student Survey site and OfS NSS guidance.

Student Voice AI is a university spin‑out led by an academic who still lectures at a Russell Group university. Every valid comment and sentence is interpreted in context, informed by years of training on survey responses from 100+ institutions to PTES/PRES (see PTES and PRES), alongside NSS, module evaluation, student experience and welcome surveys (including work with UCL, KCL, LSE, Edinburgh, Leeds).

Why institutions choose Student Voice Analytics

• Built for UK HE: themes and categories designed for universities (not generic CX or marketing taxonomies).
• Deterministic ML for categorisation: reproducible, versioned methods; no LLMs in the classification loop.
• All‑comment coverage: every valid comment is categorised—no sampling or quota blind spots.
• Sector benchmarking: understand what’s typical vs distinctive to prioritise action across NSS/PTES/PRES/UKES.
• TEF‑style outputs: panel‑friendly write‑ups, traceability and evidence aligned with the TEF.

How it works — Same day comment analysis

1. Ingest: securely import comments from NSS, PTES, PRES, UKES, and module evaluations.
2. Classify & score: UK‑HE–tuned deterministic ML (themes, sentiment, intensity, drivers); no LLMs in the classifier.
3. Benchmark: compare institutional patterns against the sector to surface distinctiveness.
4. Publish: deliver insight packs, dashboards and TEF‑style narrative evidence you can reuse.
5. Re‑run: versioned, reproducible runs on subsequent cycles to evidence change over time.

Outputs

Outputs are structured to feed directly into Board papers, TEF submissions, and programme enhancement plans.

• Executive summary of drivers and priorities.
• Theme/category deep‑dives (assessment & feedback, teaching, resources, support, community, employability, etc.).
• Distinctiveness view (over/under‑index vs sector).
• Subject/discipline cuts (CAH/HECoS where provided) and equity lenses (your EDI segments).
• TEF‑style narratives with traceability to source comments (TEF alignment).

Trusted by world-leading higher education institutions

Governance & reproducibility

• Same input, same output: deterministic methods mean your results are reproducible across cycles.
• All‑comment, no sampling: complete coverage avoids avoidable bias.
• Versioned runs & run sheets: lock models and prompts for auditability across years.
• UK GDPR–aligned processing: data pathways and residency appropriate for your institution; see ICO UK GDPR guidance and international data transfers.

Compare & compatibility

Student Voice Analytics complements or replaces suite add‑ons and general text‑analytics. If you are standardised on a survey platform, validate coverage %, taxonomy fit, benchmarks and explainability first: Qualtrics Text iQ, Blue/MLY, or general text‑analytics such as Relative Insight. For small researcher‑led deep dives, NVivo remains strong but is slower for institutional cycles.

We encourage head‑to‑head evaluations on your real free‑text (e.g., NSS) against these alternatives.

What's included in Student Voice Analytics by Student Voice AI

• Annual NSS benchmarking against the sector (NSS context).
• AI‑generated narrative reports (positives/negatives and suggestions for improvement) with internal academic/editorial QA before release.
• Outputs by organisation (school/faculty), programme and level (UG/PGT/PGR).
• Internal QA precedes publication; reproducibility and traceability materials are included for panels.
• Support for migration from suite add‑ons (e.g., MLY or Text iQ) with method alignment.