Making a #Stepchange? Investigating the Alignment of Learning Analytics and Student Wellbeing in United Kingdom Higher Education Institutions

Abstract

In recent years there has been growing concern around student wellbeing and in particular student mental-health. Numerous newspaper articles (Ferguson, 2017; Shackle, 2019) have been published on the topic and a BBC 3 documentary (Byrne, 2017) was produced on the topic of student suicide. These have coincided with a number of United Kingdom Higher Education sector initiatives and reports, the highest profile of these being the Universities United Kingdom “#StepChange” report (Universities UK, 2017) and the Institute for Public Policy Research “Not By Degrees” report (“Not by Degrees: Improving Student Mental Health in the UK’s Universities” 2017). Simultaneously, learning analytics has been growing as a field in the United Kingdom, with a number of institutions running services predominantly based on student retention and progression, the majority of which make use of the Jisc Learning Analytics service. Much of the data used in these services is behavioral data: interactions with various IT systems, attendance at events and/or engagement with library services. Wellbeing research indicates that since changes in wellbeing, are indicated by changes in behavior, these changes could be identified via learning analytics. Research has also shown that students react very emotively to learning analytics data and that this may impact on their wellbeing. The 2017 Universities United Kingdom (UUK) #StepChange report states: “Institutions are encouraged to align learning analytics to the mental health agenda to identify change in students’ behaviors and to address risks and target support.” (Universities UK, 2017). This study was undertaken in the 2018/19 academic year, a year after the launch of the #StepChange framework and after the formal transition of Jisc’s learning analytics work with partner HEIs to a national learning analytics service. With further calls for whole institutional responses to address student wellbeing and mental health concerns, including the recently published University Mental Health Charter this study aims to answer two questions. Firstly, is there evidence of the #StepChange recommendation being adopted in current learning analytics implementations? Secondly, has there been any consideration of the impact on staff and student wellbeing and mental health resulting from the introduction of learning analytics? Analysis of existing learning analytics applications have found that there is insufficient granularity in the data used to be able to identify changes in an individual’s behavior at a required level, in addition this data is collected with insufficient context to be able to truly understand what the data represents. Where there are connections between learning analytics and student support these are related to student retention and academic performance. Although it has been identified that learning analytics can impact on student and staff behaviors, there is no evidence of staff and student wellbeing being considered in current policies or in the existing policy frameworks. The recommendation from the 2017 Stepchange framework has not been met and reviews of current practices need to be undertaken if learning analytics is to be part of Mentally Healthy Universities moving forward. In conclusion, although learning analytics is a growing field and becoming operationalized within United Kingdom Higher Education it is still in its reactive infancy. Current data models rely on proxies for student engagement and may not truly represent student behaviors. At this time there is inadequate sophistication for the use of learning analytics to identify student wellbeing concerns. However, as with all technologies, learning analytics is not benign, and changes to ways of working impact on both staff and students, wellbeing professionals should be included as key stakeholders in the development of learning analytics and student support policies and wellbeing considerations explicitly mentioned and taken into account.