Automated insightful drill-down recommendations for learning analytics dashboards

Abstract

The big data revolution is an exciting opportunity for universities, which typically have rich and complex digital data on their learners. It has motivated many universities around the world to invest in the development and implementation of learning analytics dashboards (LADs). These dashboards commonly make use of interactive visualisation widgets to assist educators in understanding and making informed decisions about the learning process. A common operation in analytical dashboards is a 'drill-down', which in an educational setting allows users to explore the behaviour of sub-populations of learners by progressively adding filters. Nevertheless, drill-down challenges exist, which hamper the most effective use of the data, especially by users without a formal background in data analysis. Accordingly, in this paper, we address this problem by proposing an approach that recommends insightful drill-downs to LAD users. We present results from an application of our proposed approach using an existing LAD. A set of insightful drill-down criteria from a course with 875 students are explored and discussed.