UWE Bristol adopts Explorance MLY to analyse student feedback

In a bid to enhance the analysis of qualitative feedback from students, UWE Bristol has invested in Explorance MLY. The artificial intelligence-powered tool will be used to process feedback gathered through the National Student Survey (NSS), the Postgraduate Taught Experience Survey (PTES), and the institution's own programme evaluations.

MLY employs machine learning models tailored specifically for universities to distill actionable insights from large volumes of unstructured comments. It is designed to rapidly identify recommendations and issue alerts based on student feedback, enabling institutions to respond promptly.

UWE Bristol, which serves over 39,900 students and employs 3,800 staff, recognised the need for a new approach to collecting and deciphering expansive student feedback. Heather Moyes, Academic Registrar at UWE Bristol, noted, "We want to be able to drill down into qualitative feedback given by students, and through MLY, we are able to do that in a smarter, quicker way."

Previously, the university relied on a less sophisticated tool and manual coding, which was time-consuming and inadequate in handling a higher volume of free-text comments. Moyes explained, "As part of our new approach to surveys, we were aware that higher response rates would bring a lot more free-text comments, which meant we needed a different solution. The tool we used previously was not used extensively in the institution, and ultimately the licence was not renewed, so we had to analyse qualitative feedback ourselves through our own manual coding which took a long time."

By adopting MLY, UWE Bristol aims to focus less on the manual processing of data and more on actionable outcomes. "Now, rather than spending lots of time finding out what students are telling us, MLY allows us to focus on what we do about it," said Moyes.

After conducting market research into AI/machine learning options, UWE Bristol decided to acquire a site licence for MLY earlier this year. Moyes secured support from the Vice-Chancellor's Executive team, citing MLY's potential to expedite decision-making and bolster forward planning. She elaborated, "The fact MLY is trained on the right kind of data, that the machine understands higher education and the language fed into it, is a key factor. It has the ability to do the analysis and give meaningful, accessible feedback quickly."

The university's Student Voice team initially tested MLY with survey data from the previous year for baseline comparisons. Following these tests, MLY has been used to analyse course surveys and the current year's PTES results. Moyes expressed enthusiasm for the forthcoming analysis of NSS results, stating that the speed of MLY's analytics and its capacity to reveal unforeseen patterns were particularly beneficial. "Speed is the real appeal and being able to see patterns so quickly, and also things we may not have been looking for in our own coding," she added.

John Atherton, VP Sales EMEA at Explorance, commented on the collaboration, saying, "We are delighted to be working with UWE Bristol. MLY helps Higher Education Institutions to make data-informed decisions that enhance the student experience. The development of teaching methods and course content following evaluation of their insights can lead to increased student satisfaction and engagement. The software also captures employee experience and employee learning insights." He also noted that while UWE Bristol is not currently a user of Explorance's Blue course evaluation platform, MLY has been purchased to support the university's existing system.