Description
The session will be run by Laura McBrien (Lead Designer, BPP Law School), Charlotte Bois-Pursey (Lead Designer, BPP Law School) and Tom Pieroni (Digital Learning Designer)
A popular definition of Learning Analytics is ‘the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimising learning and the environments in which it occurs’ (Siemens & Gasevic, 2012). BPP University Law School is currently undertaking a redesign of all its programmes. Underpinned by a blended approach to learning, technology and data are playing an increasingly important role. As is the case with many other universities, the use of learning technologies to facilitate activities means there are huge amounts of data generated. When leveraged appropriately, this can be of enormous benefit to learners and institutions, not only in terms of academic success but also for student wellbeing supported by the role of the Personal Tutor.
The Higher Education Commission (2016) reported on the benefits of Learning Analytics to improve student experience but recognised there were still ‘a number of issues and challenges facing the sector’. Ahern (2018) discusses the role of institutions in improving the student experience and the importance of positive and supportive relationships with personal tutors. Changes in wellbeing are often signalled by changes in behaviour (Anderson, 2015). By implementing measures to identify patterns in behaviour we have the potential to support pro-active and effective interventions for students.
But what is a ‘Positive Intervention’ and how do we minimise ‘Adverse Impacts’? Sclater (2018) asserts transparency is key, and that appropriate steps should be taken to reduce bias, ensure appropriate learner autonomy and support an understanding of ethical practice. This Tweet Chat seeks to explore the sector’s approach to developing and evaluating Positive Interventions and understand the potential role of Learning Analytics in Personal Tutoring.