Abstract: It has arguably never been easier to gain access to the scholarly literature, as a range of different online databases and platforms (such as Google Scholar) offer access. However, as the sheer scale of information available online has grown, algorithms are increasingly used in order to deal with searching – and this is also the case in relation to the academic literature, with search results often pre-sorted ‘by relevance’. However, how relevance is defined is not clear, varies according to different platforms, and may carry a risk of compounding biases present in scholarly publishing. In this session, findings will be reported from a recent mixed-methods study undertaken with the goal of exploring the extent to which relevance ranking algorithms are used and defined, and understanding academics’ beliefs and assumptions about how such rankings work. Furthermore, potential future directions in relation to algorithmic decision-making in Higher Education will be discussed.

Bio: Dr Katy Jordan is a Lecturer in Technology-Enhanced Learning in the Department of Educational Research, Lancaster University. She holds a PhD from the Institute of Educational Technology at the Open University, UK. Her research interests broadly sit at the intersection of educational research and internet studies, and she has published research on a range of topics, including social media in higher education, massive open online courses, and gender equity and educational technology.