Visualisation of key splitting milestones to support interventions

The paper presents an approach to help staff responsible for running courses by identifying key milestones in the educational process, where the paths of successful and unsuccessful students started to split. By identifying these milestones in the already finished courses, this information can be used to plan the interventions in the next runs. This is achieved by finding the earliest time when the differences in behaviour or key performance metrics of unsuccessful students start to become significant. We demonstrate this approach in two case studies, one focused on a course level analysis and the latter on a whole academic year. This suggests its generic nature and possible applicability in various Learning Analytics scenarios

Investigating Influence of Demographic Factors on Study Recommenders

Recommender systems in e-learning platforms, can utilise various data about learners in order to provide them with the next best material to study. We build on our previous work, which defines the recommendations in terms of two measures (i.e. relevance and effort) calculated from data of successful students in the previous runs of the courses. In this paper we investigate the impact of students’ socio-demographic factors and analyse how these factors improved the recommendation. It has been shown that education and age were found to have a significant impact on engagement with materials