Higher Education in the UK operates under a regulatory framework, the Office for Students (OfS). The University sector needs to report on a series of data metrics, where education has been quantified into measurable outcomes. These are focused on continuation and completion of studies, as well as readiness and ability to secure professional work. However, recent literature has documented the complexity of computing metrics in an environment in which universities are in constant transition and adaptation, and how these adaptive processes impact student transitions, including from university to graduate work. Thus collecting data with precision and fair statistical assessment of outcomes across the sector remains a challenge for the government and the sector alike.

Learning analytics is a highly contested field which is implemented and used in very different ways across the sector. In some cases, the collection of data places greater emphasis on institutional compliance for reputational protection and as tool for data driven narrative creation. At its most effective, it places the learner at the centre of the process and as the primary audience for its output. Emerging trends point to how it is increasingly embedded within day-to-day activities that encompass learners, educators and the institution. Our research indicates that it is most impactful when it supports data informed pedagogic decisions making.

Research we have conducted suggests three data processes: collection, collation and finally interpretations where the institutional data gathered is actioned through very different strategic lenses. Best practice seeks to use this data to inform strategic and operational decisions and to focus on the student experience, with a clear pedagogic rationale underpinning the sharing of data, that genuinely moves the student learning journey forward. The use and role of data can be characterised as a tool to defend the institution from external scrutiny; an intrinsic tool to inform course development or as instigator for dialogue (including self dialogue) by the learner.

The learning design that frames and encompasses learning analytics impacts significantly on the user. It can often be cold, dehumanising, heavily protected and context free with the data stored in what we term as a ‘data dungeon’. But it can be interpreted as a ‘data engine room’ driving forward the curricula and learning agenda; it can, we argue, at its cutting edge, frame ‘data dialogue’, shining light into the data dungeon. This paper will draw upon these themes and suggest a maturity model to ensure the data collected has meaning, use and value and contributes to a greater understanding of the measurement and understanding of learning gain.

Keywords: Learning analytics, learner analytics, learning gain, data, pedagogy.