The Contribution of Dispositional Learning Analytics to Precision Education

Precision education requires two equally important conditions: accurate predictions of academic performance based on early observations of the learning process and the availability of relevant educational intervention options. The field of learning analytics (LA) has made important contributions to the realisation of
the first condition, especially in the context of blended learning and online learning. Prediction models that use data from institutional information systems and logs of learning management systems have gained a good reputation in predicting underperformance and dropout risk. However, less progress is made in resolving the second condition: applying LA generated feedback to design educational interventions. In our contribution, we make a plea for applying dispositional learning analytics (DLA) to make LA precise and actionable. DLA
combines learning data, as in LA, with learners’ disposition data measured through self-report surveys. The advantage of DLA is twofold: first, it improves the accuracy of prediction, specifically early in the module, when limited LMS trace data are available. Second, the main benefit of DLA is in the design of effective interventions: interventions that focus on addressing individual learning dispositions that are less developed but important for being successful in the module. We provide an empirical analysis of DLA in an introductory mathematics
module, demonstrating the important role that a broad range of learning dispositions can play in realising precision education.