Checking for ‘Checkpoints’: Using course design to redefine engagement in Early-warning system learner analytics

In order to maintain pace with rising expectations to provide an ‘excellent learning environment’, higher education institutions across the world are turning to learner analytics to help allocate resources efficiently. However, the exponential increase of digital learning technologies has resulted in learner analytics sharing the same practical and ethical concerns as ‘big data’ in the wider context. This study provides an important ‘proof-of-concept’ that learner analytics is better served by data from theory driven course design, and not more data. We explore the potential of learner analytics combined with course design that incorporates regular, automated, low-stakes assignments to provide ‘checkpoints’ of student engagement. We show for a cohort of 424 foundation year students that attainment is best predicted by ‘checkpoint’ submission and not by a host of demographic and behavioural variables that have previously been identified as ‘early-warning indicators’. To conclude, we identify how the practice of integrating learner analytics and course design can help us better align our practice with ethical use of data guidelines for learner analytics and the recommendations from the literature.