Abstract
Learning analytics (LA), as research and practice, has been committed to contributing to understanding students’ learning and the contexts in which their learning occurs since its emergence in 2011. Core to learning analytics is the measurement, collection, analysis and use of student data. As higher education becomes increasingly digitised and datafied, institutions have access to not only more data, but also a variety, velocity and granularity of data. However, there is also increasing evidence of data poverty - referring not only to students’ unable to afford data, but also institutions without, inter alia, the data infrastructure and analytical skills to realise the potential of LA. In both cases, the fiduciary duty of higher education institutions to provide enabling and supportive learning environments are severely compromised. In this conceptual paper, we provide an introduction to the notion of data poverty as it relates to both students and institutions before mapping key dimensions of student data poverty and their implications for LA. We also discuss institutional data poverty before concluding with a number of pointers to move from ‘not yet’ to ‘yes we can’ in providing evidence-led teaching and student support.