Publication

Learning analytics (LA) provides insight into student performance and progress, allowing for targeted interventions and support to improve the student learning experience. Uses of LA are diverse, including measuring student engagement, retention, progression, student well-being and curriculum development. This article provides perspectives on the uses of LA in the UK through the analysis of an expert-led panel discussion held in June 2022. Two institutional case studies and a general overview from an LA service are presented, outlining examples of LA from both an institutional and national viewpoint. Following this, this article analyses the panel discussion themes in relation to the literature, covering both the data quality procedures and practices for learning, teaching and assessment. Outcomes and benefits from case studies are highlighted, which serve as best practice for other Higher Education institutions.

This paper offers guidance on employing open and creative methods for co-designing critical data and artificial intelligence (AI) literacy spaces and learning activities, rooted in the principles of Data Justice. Through innovative approaches, we aim to enhance participation in learning, research and policymaking, fostering a comprehensive understanding of the impact of data and AI whilst promoting inclusivity in critical data and AI literacy. By reflecting on the Higher Education (HE) context, we advocate for active participation and co-creation within data ecosystems, amplifying the voices of educators and learners. Our methodology employs a triangulation model: initially, we conduct interpretative analyses of literature to gauge best practices for curriculum development in HE; then, we examine frameworks in data justice and ethics to identify principles and skills applicable to undergraduate, postgraduate and academic development programs; finally, we explore proposals for critical, creative, ethical, open and innovative ideas for educators to integrate data and AI into their practice.