A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning

In recent years, automated learning systems are widely used for educational and training purposes within various organisations including, schools, universities and further education centres. A common challenge for automated learning approaches is the demand for an effectively well-designed and fit for purpose system that meets the requirements and needs of intended learners to achieve their learning goals. This paper proposes a novel approach for automated learning that is capable of detecting changing trends in learning behaviours and abilities through the use of process mining techniques. The goal is to discover user interaction patterns, and respond by making decisions based on adaptive rules centred on captured user profiles. The approach applies semantic annotation of activity logs within the learning process in order to discover patterns automatically by means of semantic reasoning. Therefore, our proposed approach is grounded on Semantic modelling and process mining techniques. To this end, it is possible to apply effective reasoning methods to make inferences over a Learning Process Knowledge-Base that leads to automated discovery of learning patterns or behaviour