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

Okoye, Kingsley and Tawil, Abdel-Rahman H. and Naeem, Usman and Bashroush, Rabih and Lamine, Elyes (2014) A Semantic Rule-based Approach Supported by Process Mining for Personalised Adaptive Learning. In: Procedia Computer Science. Elsevier.

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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.

Item Type: Book Section
Identification Number: https://doi.org/10.1016/j.procs.2014.08.031
Uncontrolled Keywords: process modelsemantic rulesprocess mininguser profilelearning behaviourevent logs
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Depositing User: Oana-Andreea Dumitrascu
Date Deposited: 29 Jun 2017 13:55
Last Modified: 22 Mar 2023 12:02
URI: https://www.open-access.bcu.ac.uk/id/eprint/4763

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