A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning

Currently, automated learning systems are widely used for educational and training purposes within various organisations
including, schools, universities and further education centres. There has been a big gap between the extraction of useful patterns
from data sources to knowledge, as it is crucial that data is made valid, novel, potentially useful and understandable. To meet the
needs of intended users, there is requirement for learning systems to embody technologies that support learners in achieving their
learning goals and this process don’t happen automatically. This paper propose 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 within learning processes, 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.

Paper presented at The 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2014, September 22-25, 2014, Halifax, Nova Scotia, Canada