The Student Voice Weekly

Dr Stuart Grey
  • 0.0 (0)
  • Education
  • Updated Weekly

The Student Voice Weekly is a short podcast for UK higher education leaders who want to turn student feedback, policy and research into practical action. Each week, Dr Stuart Grey, founder of Student Voice AI and Senior Lecturer at the University of Glasgow, unpacks the latest evidence on student voice, assessment, feedback, regulation and institutional improvement. Expect concise briefings on HE research, OfS and QAA developments, NSS and survey practice, and practical ways to use student comments more rigorously.

  1. 6d ago

    Where your student comments actually go

    This week, Dr Stuart Grey steps away from the usual research and sector-news format to talk directly about data security and provenance. Prompted by Student Voice's recent move to serve Irish institutions from an EU data centre, Stuart explains how regional residency and model provenance work together. UK customer data remains entirely in the UK, while Irish customer data is processed in the EU. Categorisation and sentiment analysis use deterministic machine-learning models; locally run LLMs are used only for summarisation. In This Episode - Why student comments need to be treated as sensitive evidence rather than ordinary spreadsheet text. - Why UK customer data remains entirely resident in the UK. - What EU data residency provides for Irish institutions-and what it does not answer by itself. - Why categorisation and sentiment analysis use deterministic machine-learning models. - Why LLMs are used only for summarisation and run locally on controlled hardware. - What data provenance means in practical terms. - Why model, taxonomy, prompt, and processing versions matter for credible trends. - Five questions to ask before uploading real student data to an analysis tool. Practical Resources - Student comment analysis governance checklist: https://www.studentvoice.ai/resources/student-comment-analysis-governance-checklist/ - Student Voice Analytics governance and reproducibility: https://www.studentvoice.ai/student-voice-analytics/ - Student Voice Analytics and generic LLMs compared: https://www.studentvoice.ai/compare/student-voice-analytics-vs-generic-llms/ - ICO guidance on UK GDPR: https://ico.org.uk/for-organisations/uk-gdpr-guidance-and-resources/ - ICO guidance on international data transfers: https://ico.org.uk/for-organisations/data-protection-and-the-eu/data-protection-and-the-eu-in-detail/the-uk-gdpr/international-data-transfers/ Practical Takeaway Pick one student comment and map its full journey from upload to analysis, reporting, retention, and deletion. Any point that cannot be clearly explained deserves attention before real student data is processed. About This Recording This episode was recorded by Dr Stuart Grey. The transcript was prepared from the final recording and lightly corrected for names, technical terminology, and readability. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    Where your student comments actually go

  2. Aug 7

    Let students shape the question

    This week, Dr Stuart Grey asks who gets to decide what universities ask students in the first place. Drawing on papers he read this week, his own teaching, and Student Voice's national NSS comment analysis, Stuart explores what changes when students help frame the question, produce the evidence, and interpret what it means. In This Episode - Why student-led consultation can change the quality of wellbeing evidence. - What Bangor University's student consultants added by working across the whole evidence pipeline. - What Student Voice's Feedback and Student Support categories reveal beyond the headline average. - What a study of more than 1,200 students reveals about self-doubt and feedback-seeking. - How CAH codes support like-for-like subject comparisons without becoming a league table. - Why a category is only a starting point when one comment can cover several experiences. - How earlier student involvement can carry through into curriculum review and routine practice. Research - Student-led wellbeing strategy works best when students lead the consultation: https://www.studentvoice.ai/blog/student-led-wellbeing-strategy-works-best-when-students-lead-the-consultation/ - Self-doubt makes students watch for feedback, but hesitate to ask for it: https://www.studentvoice.ai/blog/self-doubt-makes-students-watch-feedback-but-hesitate-to-ask/ Across the Sector - Advance HE says student voice should start before consultation: https://www.studentvoice.ai/blog/advance-he-student-voice-should-start-before-consultation/ - Northampton's Inclusive Curriculum Toolkit shows how student listening can reshape assessment and feedback: https://www.studentvoice.ai/blog/northamptons-inclusive-curriculum-toolkit-student-listening-assessment-feedback/ Student Voice Evidence - NSS open-text analysis methodology for UK higher education: https://www.studentvoice.ai/resources/nss-open-text-analysis-methodology/ - What UK students say about feedback: https://www.studentvoice.ai/category/feedback/ - What UK students say about student support: https://www.studentvoice.ai/category/student-support/ Practical Implication Bring students into the evidence process before the questions and proposed solutions are fixed. Use categories and CAH comparisons to locate patterns, then read the comments in context and turn the shared interpretation into routine practice. About This Recording This episode was recorded by Dr Stuart Grey. The transcript was prepared from the edited recording and lightly corrected for names, terminology, and readability. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    Let students shape the question

  3. Jul 31

    Designing participation that students can use

    This week, Dr Stuart Grey looks at how universities can design student voice around usable space, meaningful expression, a real audience, and visible influence. The episode considers a Manchester Metropolitan University case study applying Lundy's participation model, a scoping review of how higher education research defines belonging, Leicester's NSS 2026 results, and the consistency questions raised by Swansea University's Quality Enhancement Review. In This Episode - Why adding a feedback channel does not necessarily widen participation. - How space, voice, audience, and influence provide a practical audit for student voice. - What a reflective departmental case study can and cannot demonstrate. - Why belonging measures need definitions grounded in student experience. - How open comments can explain what students recognise as visible action. - Why strong partnership structures still need consistent assessment and support rules. Research - Student voice gets stronger when participation is designed, not assumed: https://www.studentvoice.ai/blog/student-voice-gets-stronger-when-participation-is-designed-not-assumed/ - Belonging surveys are stronger when students define what belonging means: https://www.studentvoice.ai/blog/belonging-surveys-are-stronger-when-students-define-belonging/ Across the Sector - Leicester's NSS 2026 results show why student voice gains need visible follow-through: https://www.studentvoice.ai/blog/leicester-nss-2026-results-student-voice-visible-follow-through/ - Swansea's QER says strong student partnership still needs more consistent assessment and support rules: https://www.studentvoice.ai/blog/swansea-qer-student-partnership-assessment-support-rules/ Practical Implication Take one existing student voice route and identify where students can contribute, how they can express a view, who is expected to respond, and what evidence will show that their contribution had influence. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    Designing participation that students can use

  4. Jul 24

    When participation becomes influence

    This week, Dr Stuart Grey looks at the difference between students participating in teaching and feeling able to influence it. The episode examines a study of 1,713 final-year students in Colombia, where expressive voice accounted for a modest part of the relationship between collaborative learning and academic achievement. It then considers research on care and engagement, Sheffield Hallam's move away from a university-wide module evaluation process, and the evidence standards universities still need when student voice becomes more local. In This Episode - Why participation and influence are different dimensions of student engagement. - What the Colombian study can and cannot tell us about expressive voice and achievement. - How care, clarity, interaction, and assessment design shape engagement. - Why changing the unit of a survey may not resolve weak participation. - What institutional governance is needed when Schools use different feedback methods. - How open comments can distinguish participation, influence, response, care, and clarity. Research - Student voice improves outcomes when students can shape teaching: https://www.studentvoice.ai/blog/student-voice-improves-outcomes-when-students-can-shape-teaching/ - Student engagement depends on visible care, not just teaching technique: https://www.studentvoice.ai/blog/student-engagement-depends-on-visible-care-not-just-teaching-technique/ Across the Sector - Sheffield Hallam's module evaluation reset shows why local student voice still needs governance: https://www.studentvoice.ai/blog/sheffield-hallam-module-evaluation-local-student-voice-governance/ - OfS NSS student characteristics data adds provider typologies, but key equity splits are missing: https://www.studentvoice.ai/blog/ofs-nss-student-characteristics-data-provider-typologies-missing-equity-splits/ Practical Implication If module evaluation becomes more local, retain a small shared evidence framework. Record what was asked, who could respond, how the evidence was interpreted, who owns the response, and what students were told afterwards. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    When participation becomes influence

  5. Jul 17

    What QAA wants universities to do after NSS

    This week, Dr Stuart Grey focuses on QAA's response to NSS 2026 and what universities need to do once the results-day headlines have passed. The episode looks at internal enhancement, weaker experiences among part-time, apprenticeship, and disabled students, the cost of taking part in student engagement, and the support student representatives need to contribute meaningfully to quality assurance. It also considers why shorter provision needs faster feedback loops. In This Episode - Why improving NSS averages should not close down the conversation about subgroup gaps. - What QAA's post-results message means for internal enhancement work. - How time, money, accessibility, and recognition shape who can participate in student voice. - Why student representatives need clear papers, briefings, mentoring, and wider survey evidence. - How short-cycle provision changes the timing of student feedback. Student Voice Practice Use NSS as the beginning of an internal quality conversation. Bring scores, subgroup patterns, and open comments together, then connect that evidence to supported student representatives and named institutional owners. When analysing comments, distinguish the student experience itself from barriers to participation and evidence about follow-through. These patterns require different actions and often belong to different teams. Research Spotlight - Student representation in quality assurance needs training, trust, and usable evidence: https://www.studentvoice.ai/blog/student-representation-quality-assurance-needs-training-trust-usable-evidence/ Across the Sector - QAA says NSS 2026 should drive internal student voice action, not just league tables: https://www.studentvoice.ai/blog/qaa-nss-2026-internal-student-voice-action/ - QAA's short-cycle course guidance says student feedback must move faster for the LLE: https://www.studentvoice.ai/blog/qaa-short-cycle-course-guidance-student-feedback-lle/ Practical Takeaway Bring NSS scores and comments together, check who is missing or having a weaker experience, support representatives to interpret the evidence, and record who owns the response and when students will see what changed. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/.

    What QAA wants universities to do after NSS

  6. Jul 10

    NSS results need more than headlines

    This week, Dr Stuart Grey focuses on NSS 2026 results day: the national headlines, the disabled student gaps that still need action, and what it takes to turn large volumes of student comments into usable evidence quickly. The episode also looks at what Student Voice AI produced for NSS customers on results day, why sentence-level labels matter, and how recent research should shape the way universities use machine-learning analysis and high-stakes teaching evaluation evidence. In This Episode - Why stronger NSS national headlines do not remove the need for subgroup analysis. - What results day looked like across Student Voice AI's NSS customer work. - Why sentence-level comment analysis helps teams avoid flattening mixed student feedback. - How Valeriya Minakova, Ryan Patterson and colleagues frame machine learning as a complement to academic judgement. - Why Shaylen Stone, Gerard Jefferies, Megan Lee and Cindy Davis's wellbeing research is a warning about high-stakes evaluation use. - Why disabled student gaps in organisation, management, and student voice need comment-level evidence alongside the scores. Student Voice Practice NSS results become more useful when teams read national context, local score movement, and student comments together. Comment analysis should preserve enough detail for staff to see what students actually meant, while keeping outputs reviewable and proportionate. Sentence-level labelling helps separate praise, concerns, and mixed experiences within the same comment. That makes the evidence easier to route to the right people and reduces the risk that one average score carries too much of the story. Research Spotlight - Can Machine Learning Help Instructors Make Sense of Student Evaluation Comments?: https://www.studentvoice.ai/blog/machine-learning-mid-semester-teaching-evaluations/ - High-stakes teaching evaluations can damage academic wellbeing: https://www.studentvoice.ai/blog/high-stakes-teaching-evaluations-can-damage-academic-wellbeing/ Across the Sector - NSS 2026 results rise on student voice, but disabled student gaps still need action: https://www.studentvoice.ai/blog/nss-2026-results-student-voice-disabled-student-gaps/ - OfS updates modular outcomes for the LLE, and why continuous student feedback matters: https://www.studentvoice.ai/blog/ofs-modular-outcomes-lle-continuous-student-feedback/ Practical Takeaway Treat NSS results day as the start of evidence work, not the end of reporting. Use the national headlines, but read them alongside subgroup gaps and the comments students wrote in their own words. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    NSS results need more than headlines

  7. Jul 3

    Student feedback should start a dialogue

    This week, Dr Stuart Grey starts with Daniel Robson and Helena Lim's Wonkhe piece on King's College London, where the NSS comment analysis work was delivered in collaboration with Student Voice AI. The episode discusses why faster comment analysis matters only when it preserves enough detail for human review, academic judgement, and visible action while universities still have time to respond. In This Episode - What the King's College London example shows about turning NSS comments into usable evidence quickly. - Why student feedback should not be treated as a direct verdict on teaching quality. - Why NSS timing makes local, earlier feedback systems more important. - How VLE feedback and reattempt loops change what universities should ask about digital learning. - Why faster comment analysis matters only when it leads to earlier human review and visible action. - How to separate comments about process, timing, interpretation, digital learning design, and trust. Student Voice Practice Feedback analysis should preserve enough context for dialogue. A useful approach distinguishes whether comments are about process, timing, interpretation, learning design, or trust, then routes the evidence to people who can act while action is still possible. The King's example matters because it shows the practical value of keeping a traceable line between what students wrote, how comments were categorised, and the judgement course teams then make. Research Spotlight - Student evaluations should inform dialogue, not replace academic judgement: https://www.studentvoice.ai/blog/student-evaluations-should-inform-dialogue-not-replace-academic-judgement/ - VLEs scale better when feedback and reattempt are built in: https://www.studentvoice.ai/blog/vles-scale-better-when-feedback-and-reattempt-are-built-in/ Across the Sector - Wonkhe's AI feedback analysis case shows how universities can act on NSS comments sooner: https://www.studentvoice.ai/blog/wonkhe-ai-feedback-analysis-nss-comments-action/ - Wonkhe's sector data warning shows why NSS and student feedback still arrive too late: https://www.studentvoice.ai/blog/wonkhe-sector-data-warning-nss-student-feedback-too-late/ From the Archive - NSS open-text analysis methodology for UK HE: https://www.studentvoice.ai/resources/nss-open-text-analysis-methodology/ - The current understanding of student voice in assessment and feedback: https://www.studentvoice.ai/blog/the-current-understanding-of-student-voice-in-assessment-and-feedback/ - How to enhance student voice in university governance: https://www.studentvoice.ai/blog/how-to-enhance-student-voice-in-university-governance-through-student-representation/ Practical Takeaway Treat student feedback as the beginning of a structured conversation. Keep enough detail in the comments to show what students meant, who needs to respond, and whether action can still happen in time. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    Student feedback should start a dialogue

  8. Jun 26

    What students actually mean when they talk about AI

    This week, Dr Stuart Grey discusses why universities need sharper AI-related student feedback questions, especially as guidance moves from broad policy statements into modules, assessments, and local teaching practice. The episode covers AI attitude survey design, Jisc's latest AI guidance and governance work, student mini-publics, and how universities can separate AI comments by usefulness, clarity, confidence, fairness, and support need. In This Episode - Why broad "do you use AI" or "are you concerned about AI" questions no longer give universities enough evidence to act on. - Why student AI attitudes need to be separated into usefulness, identity and confidence, and concern. - How module-level and assessment-level AI guidance changes what universities should ask students. - Why human-in-the-loop governance matters when AI is used to summarise or interpret student comments. - How to code AI-related comments so teams can distinguish unclear rules, low trust, fairness concerns, and support needs. - Why feedback loops matter when students are asked for views on fast-moving institutional decisions. Student Voice Practice AI feedback should not be treated as one broad theme. A useful analysis should distinguish whether students are talking about practical usefulness, unclear rules, confidence using tools appropriately, fairness between students, detection and false positives, staff inconsistency, or the need for better support. Research Spotlight - AI attitude surveys need to separate usefulness, self-expression, and concern: https://www.studentvoice.ai/blog/ai-attitude-surveys-need-separate-usefulness-self-expression-and-concern/ - Student mini-publics only matter if universities can show what changed: https://www.studentvoice.ai/blog/student-mini-publics-only-matter-if-universities-can-show-what-changed/ Across the Sector - Jisc's June HE AI meetup says universities need sharper student feedback on AI guidance: https://www.studentvoice.ai/blog/jisc-june-he-ai-meetup-student-feedback-guidance/ - Jisc's 'human in the loop' pilot sharpens AI governance for student feedback evidence: https://www.studentvoice.ai/blog/jisc-human-in-the-loop-ai-student-feedback-governance/ From the Archive - What learning resources do business management students say they need most?: https://www.studentvoice.ai/blog/learning-resources-for-business-management-students/ - Do extracurricular activities enhance history students' academic success?: https://www.studentvoice.ai/blog/history-students-perspectives-on-extracurricular-activities/ - What assessment methods work in therapy education?: https://www.studentvoice.ai/blog/assessment-methods-in-counselling-psychotherapy-and-occupational-therapy-education/ Practical Takeaway Dig into what students mean when they talk about AI. Separate usefulness from confidence, confidence from clarity, and clarity from concern, then tag comments in a way that shows whether the problem is unclear rules, low trust, or support needs. Subscribe Subscribe to The Student Voice Weekly: https://www.studentvoice.ai/blog/newsletter/

    What students actually mean when they talk about AI
See All (25)

About

The Student Voice Weekly is a short podcast for UK higher education leaders who want to turn student feedback, policy and research into practical action. Each week, Dr Stuart Grey, founder of Student Voice AI and Senior Lecturer at the University of Glasgow, unpacks the latest evidence on student voice, assessment, feedback, regulation and institutional improvement. Expect concise briefings on HE research, OfS and QAA developments, NSS and survey practice, and practical ways to use student comments more rigorously.

Information

  • Creator
    Dr Stuart Grey
  • Years Active
    2026
  • Episodes
    25
  • Rating
    Clean
  • Copyright
    © Copyright 2026 Student Voice Systems Limited
  • Show Website
    The Student Voice Weekly