What makes feedback useful to students?

Dr Stuart Grey explores what our NSS comment data says about feedback: its prominence, changes in tone and differences between disciplines. He connects those findings with an institution-wide review of timing, clarity and opportunities to use advice, alongside a brief research finding about perceived fairness.

In this episode:

Feedback appears in 13.8% of classified comments in 2026, almost unchanged from 2025.
A longer view: mentions rose from 10.5% in 2023 while sentiment became less negative.
Similar frequency and different sentiment in arts and physical sciences, with a higher mention rate in psychology.
Keeping appreciation for advice and difficulties using it visible together.
Reviewing assessment sequences, clarity and priorities through existing survey comments.
Finding more detailed feedback results by discipline on the website.

Chapters

00:00 - What makes feedback useful?
00:44 - Feedback trends in the NSS comments
02:09 - Differences between disciplines
03:38 - Explore feedback findings on the website
04:00 - Timing and the assessment sequence
05:11 - Clarity and priorities for improvement
05:57 - Institution-wide review and positive accounts
06:44 - Research on usable feedback and fairness
07:13 - Connecting comments with process evidence
07:50 - Takeaways and further reading

Detailed findings and research

Explore feedback findings across the disciplines we study.
NSS open-text analysis methodology.
Students judge feedback comments as fairer when they are usable. David Playfoot, Ruth Horry and Aimee E. Pink, Fairly useful feedback.

Mention rates describe classified comments within the relevant year or subject group. The sentiment index summarises language on a −100 to +100 scale. The proposed institutional review and imagined assessment examples are Stuart's practical applications.

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Transcript

Hi, and welcome back to Student Voice Weekly. I'm Dr Stuart Grey, founder of Student Voice AI, and today I'd like to talk about what students are saying about feedback. So there are some interesting differences in our data, both over time and between disciplines that help us think more carefully about what makes feedback useful.

So I still teach part-time at University of Glasgow so this connects very directly with my teaching practice. We spend time reading students' work and responding to it. The question I want to keep in view is what happens after that? After we've responded to students' work, what do they think about it? Does the advice arrive at a useful point, and can the student work out what to do with it?

In our analysis of the 2026 NSS comments, feedback appears in five thousand six hundred and fifty responses. That's about fourteen percent of the comments we classified, roughly one in seven. The previous year, it was thirteen point nine percent, so just less. So the latest year-on-year picture is very steady in terms of how often feedback comes up in student comments.

The tone has moved a little bit. Our sentiment index went from minus thirteen point five to minus twelve point five. That's a modest improvement with the overall balance still negative, though. The index summarises the language students use about feedback on a scale from minus one hundred to plus one hundred.

Looking further back makes the picture a little more interesting. In 2023, feedback appeared in 10.5% of classified comments. By 2026, that had risen to 13.8%. Across the same period, sentiment moved from -22.9 to -12.5. Feedback became more prominent in the comments while the language about it became less negative.

That is why I would keep frequency and tone and sentiment alongside each other. More mentions can include students describing something that helped them, for instance. Equally, a topic can remain prominent while experiences with it improve. If we only looked at how often feedback appeared, we would miss part of that change.

The differences between disciplines are much larger than that latest movement, however. So in design, creative and performing arts, feedback appears in 14.8% of the classified comments with a positive sentiment index of 11.4. In the physical sciences, however, the mention rate is similar at 14.1%, but sentiment is -25.1. So that's a huge difference in the tone for two different subject groups where feedback is mentioned about as often. So a chart of topic frequency alone would make them look fairly similar, but reading the sentiment alongside that, it really tells us there's a very different experience to investigate with these students.

If we look at psychology, for instance, this adds another pattern. So feedback appears in about 18% of classified comments in psychology with negative sentiment as well. So we have questions about how prominent feedback is within a subject and about how students describe it when they mention it. Both can help us decide where to look more closely within our own institution.

I would, however, be careful about jumping straight from those differences to an explanation. So assessment formats, the sequence of assignments, and opportunities to discuss advice are all things I'd want to examine locally. The figures help us choose the questions, but reading the relevant survey comments, as ever, helps us understand which parts of the experience students are actually describing.

If you'd like to explore these differences further, you can find the feedback findings on studentvoice.ai. The feedback section includes comparisons across broad disciplines and more detailed subject areas alongside the changes over time. I'll put a direct link in the show notes for you there. You can look at the subjects relevant to your work and see how their mention rates and sentiments compare.

For me, the next step is to bring that kind of analysis into the institution. So timing, clarity, and a useful next action are three practical distinctions I'd use when reading the survey comments about feedback. They help us turn a broad concern about feedback into a question that someone can actually do something about.

So if we take timing first, we might have a policy saying work should be returned within a certain number of days or working days. It's useful to know whether that happens. I'd also look at where the return date sits in the student's assessment schedule. Advice can meet the deadline and still arrive after the next piece of work has been submitted, for instance. So we have to look at it holistically across a whole student course.

So if we look across the whole programme, individual turnaround figures might look reasonable while the overall sequence gives students very little opportunity to apply what they've learned. An institutional research team could read the timing concerns across these modules, then look at the assessment calendar with the colleagues responsible and perhaps make some small changes that can have a really big impact. That gives us a specific thing to work on and review.

So clarity of feedback is a different kettle of fish. A student may understand which part of the work was weaker and still be unsure what a better approach would involve. When reading comments, I'd look for the distinction between recognising a criticism and knowing how to respond to it. It can help course teams decide whether the advice needs a clearer explanation or a more concrete example, for instance.

Then there is a prioritisation of feedback. So detailed feedback can contain a lot to work on. In a local review, I would look for whether students describe knowing what to tackle first and whether later assessments give them a chance to practise it. These are questions to explore through the comments rather than making assumptions we might like to make based on the negative sentiment score by itself.

The key thing is to examine these experiences across the relevant institutional comment set, keep the survey questions and periods there with us for context, and use consistent distinctions and compare groups where that comparison makes sense. So that helps us see whether a concern recurs across schools or is concentrated in a particular course or stage of study.

I'd keep positive accounts in that review as well. If students describe feedback that helped them change their approach, we want to understand what was useful about it and keep it as an example of best practice because this gives colleagues something concrete to discuss alongside the difficulties. Also helps us to make sure the things we're doing right, that we can keep them going right and we're not just throwing the baby out with the bathwater.

There's also a research summary in this week's newsletter that fits with this. So David Playfoot, Ruth Horry, and Aimee E. Pink tested students' responses to feedback on fictional assignments. Across two experiments, usable comments received higher fairness ratings. Supportive wording had a less consistent effect. They measured perceived fairness rather than subsequent learning, but it gives us a useful reason to pay attention to what students can do with the advice we give them in our feedback.

For an institution-wide review, I'd bring the comment patterns together with the relevant process evidence. Return dates can help with the timing question. Assessment schedules can show where opportunities to use feedback advice occur. The comments can help explain what students found clear, confusing, or useful. Together, they give us a more specific basis for deciding what to change.

We then have to agree who owns that change and what we want them to do with it. What are the next stages? What is the approach to the next task? We want to keep this all consistent across the institution, across our reviews, so that we treat everyone fairly.

So what stands out to me in our data is that feedback remains very prominent. The tone has improved over a longer period, and experiences differ substantially between disciplines. That makes the detail of the comments worth reading and diving into those discipline-specific comment sets really worthwhile.

You'll find detailed feedback results and the research summary linked in the show notes and this week's newsletter, and you can sign up at studentvoice.ai. If this podcast was useful at all, please share it with a colleague working in student survey analysis or quality. Thanks for listening, and I'll speak to you next time.