Heriot-Watt adopts AI for analysing student feedback

Heriot-Watt University has acquired Explorance MLY to enhance the analysis of qualitative feedback from student surveys.

The university, which has campuses in Edinburgh, Scottish Borders, Orkney, Dubai, and Malaysia, aims to better utilise the data collected from about 27,000 students. This initiative encompasses surveys such as course/module evaluations, annual surveys, welcome week surveys, the Postgraduate Taught Experience Survey, and the Postgraduate Research Experience Survey.

Kirsty Scanlan, Director of Strategic Planning, Performance and Projects at Heriot-Watt University, noted: "We have a very healthy provision of data from these surveys, but have lacked the software to make the most of it. Open comments are a rich source yet extremely time consuming to process. Our team has been internally reviewing qualitative data, manually cleaning this as it would include personal details of staff or students themselves, and then getting that feedback to appropriate staff members."

Scanlan further stated, "As a university we want to embrace AI in appropriate and sensible ways, to support what we cannot provide, and MLY will help us to make better use of the data we are collecting. With 17,000 responses to student surveys, and on average five open-ended questions in each survey, there are around 100,000 open comments to process. Through MLY's analysis we will be able to better identify local challenges, ensure that subject/discipline areas are targeted, and provide insight to local leaders more swiftly to make staff and students' lives easier."

She added, "We also want to increase the rate of responses, particularly on internal surveys, so the more data pushed through MLY the better we can utilise it. MLY has the advantage of being informed by masses of cohorts of data from other collections around the world, so whilst it may tell us what we already know it gives us the evidence to back it up."

Richard Cooper, Business Intelligence Analyst, discovered Explorance MLY at the HESPA Annual Conference. He said, "I was not actively seeking anything else to add to our systems, but it dawned on me that MLY could be a really useful tool to help us out in the long term, with potential to make a really positive change across a number of areas."

He explained the challenges faced by the two-person team handling student surveys annually, stating, "Student surveys are looked after on an annual basis by a two-person team. These are heavy to manage, and the manual analysis of open comments became too much, too laborious. Now, with MLY software doing the analysis for us, we can work through multiple thousands of responses. Even for those students who are not responding there will also be an impact as we will now get richer insight from those that do."

Speaking about the early stages of using Explorance MLY, Cooper said: "Whilst we are just staring our journey with Explorance, they are great to work with. I attended Explorance World, which this was an eye-opening experience, and they were very welcoming even prior to being a customer. Ultimately we hope that MLY becomes transformational."

Explaining the project's alignment with the university's strategic goals, Scanlan said: "In our current strategy we have a performance indicator on student satisfaction, and this will be at the heart of our next strategy too. We have made a commitment that feedback is listened to, acknowledged, and utilised, and student experience is at the heart of process development. Heriot-Watt is unique in that we have the same academic provision in Dubai and Malaysia as we do in the UK, and student surveys are also truly global. We are keen to see how MLY helps with Malaysia where English is not first language, and consolidate our global campus. There has to be a consistent global student experience."

John Atherton, VP Sales – EMEA at Explorance, expressed: "We are delighted to have Heriot-Watt University join the growing number of institutions using Explorance MLY for AI-powered qualitative analysis. Thematically analysing free-text comments from student surveys is a huge challenge for universities, in terms of time and resources, but MLY distills data-driven actionable feedback from huge amounts of unstructured comments. With machine learning models purpose-built for higher education, MLY has specialised provision that identifies recommendations and alerts from student feedback."