How University of Nottingham Cuts Time Spent Analysing Student Feedback by 70%

About the University of Nottingham

The University of Nottingham is a pioneering university with 45,000 students across its campuses in the UK, China and Malaysia. A founding member of the Russell Group and a top 20 UK university, it combines a strong focus on teaching quality and the student experience with world-leading research. Ranked seventh in the UK for research power, Nottingham’s work drives real-world change, transforms lives and helps shape the future.

The Challenge: Turning Thousands of Comments into Timely Insight

Nottingham collects feedback through the National Student Survey (NSS) and its Nottingham Learning Experience Survey (NLES). The NLES helps the University get to the root of issues covered in the NSS by inviting non-final year respondents to comment on each of the NSS themes.

Analysing those comments used to take considerable time. The University’s Insight and Analytics team wanted to make fuller use of student feedback provided alongside their survey scores and share findings quickly enough to inform action. Claire Bench, Data Analyst, has worked with student surveys at the University of Nottingham for almost 25 years and recalled how the process had evolved.

“I used to be the person who coded individual comments, back in the day when we were literally colouring things in with highlighters on paper surveys,” Claire explained. “Then we progressed into coding in Excel and using other qualitative tools that still required manual coding. So, as you can expect, it took quite a long time from survey closure to final analysis and getting it out there.”

Meanwhile, colleagues could already access quantitative findings through the University’s reporting dashboards. They needed the comments to help explain the scores, but that richer insight arrived later.

The University also wanted to strengthen how it responded to students. “Some of the issues have always been around closing the feedback loop, ensuring that what students have told us they need to see change actually happens,” Claire said. “We need to have action plans that tell our students, as well as ourselves, what we’ve done as a result of the feedback we’ve received. It’s about being specific around what changes we’ve made and how we can inform students about those, which also encourages them to participate in such activities.”

The Solution: Applying Higher Education-Focused Analysis to NLES and NSS

In 2024 Nottingham commissioned Explorance MLY to analyse free-text responses more efficiently. After testing smaller datasets, the team’s first major project used 2025 Nottingham Learning Experience Survey (NLES) data. MLY categorises comments into themes and allows users to move from an overview of the leading topics to the individual comments behind them.

For Claire, its appeal was that it could carry out the labour-intensive first stage of the work her team had previously done manually. “When Explorance demoed MLY to us, it really appealed because it basically did what I did as a human: categorise a comment, assign themes to it, and then allow us to drill down and access specific comments very rapidly,” she revealed. “You could pull out a particular theme or list the top 10 themes and then see what was coming through. It delivered on doing that, and it takes 15 or 20 minutes to process thousands of records, down from around 12 days of my time.”

In MLY, Nottingham values a model designed around higher education feedback. “We looked at a variety of tools, but I think the fact that Explorance is HE-specific very much makes MLY different to competitors we’ve looked at in the past,” Claire said. “That Explorance are building models for the HE market is just very different to looking at text-analysis tools designed for customer feedback. The way in which MLY replicates what we did manually, the match was really good. Those were the sells for us.”

Having used MLY for its institutional survey, Nottingham joined five other UK universities in helping Explorance introduce a new AI-driven qualitative analysis model dedicated to the NSS. The model maps qualitative feedback to ten core themes, including seven aligned with NSS reporting.

In 2026, Nottingham used the model to analyse its NSS comments for the first time. Previously, those comments had largely been presented as positive or negative responses that colleagues could filter or search by keyword. The new structure allows them to explore themes across the feedback and find relevant comments without having to anticipate every word students might use.

Ufuoma Elegbede, Analyst, commented: “The reception has been great across the University, having that detailed structure for the analysis. This year, we’ve been able to code to the thematic structure of NSS, and that’s been very well received.”

The Impact: More Detailed Findings Delivered When They Are Needed

Across the NLES and NSS, Nottingham analysed 32,429 comments in 2026. The NLES surveyed 21,721 students, covering approximately 850 programmes across the University’s three UK campuses and nursing and medical sites. The team also estimates that MLY has reduced the time spent analysing feedback by 70%.

For the 2026 NSS, Nottingham made its analysis available within the same week that the results arrived. The added thematic detail did not delay the reporting timetable. Staff received more useful qualitative findings as quickly as they were accustomed to receiving the survey data. “The timeliness with which we’ve been able to provide NSS data has been really important,” Claire said. “We were offering a better service and a much better outcome for staff, who were able to find the kinds of topics they were interested in much more quickly. They were getting more in exactly the same timeframe.”

The findings feed into Nottingham’s wider approach to improvement. Each school is required to provide a written response to NLES findings and develop an action plan. Faster access to themes and underlying comments helps colleagues examine where experiences differ and consider what their response should be. The University sees the NLES as particularly useful for understanding the views of students who are still partway through their courses. Claire said colleagues are happy with the accuracy of the analysis.

Nottingham has since started using MLY to analyse Postgraduate Taught Experience Survey feedback and is considering further applications. Reflecting on the purpose behind the work, Ufuoma shared: “Our goal is not simply to categorise comments. Our goal is to improve the student experience. The technology helps us move from raw feedback to actionable insight, but the real value comes from how the wider Nottingham community uses those insights to drive improvement.”

Claire concluded: “I would absolutely recommend Explorance, and MLY, to other universities to help them with similar pain points that we’ve experienced. It’s the speed at which we can deliver in the way the University wants us to provide that feedback. Within a week of the data coming in, we can share the findings. There is significant demand for the comments to come quickly, and that’s what MLY can provide. There wouldn’t be an alternative without an AI solution. MLY is at the heart of making positive change.”