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

Okoye, Kingsley and Tawil, Abdel-Rahman H. and Naeem, Usman (2014) A Semantic Rule-Based Approach Towards Process Mining for Personalised Adaptive Learning. In: 2014 IEEE Intl Conf on High Performance Computing and Communications, 2014 IEEE 6th Intl Symp on Cyberspace Safety and Security, 2014 IEEE 11th Intl Conf on Embedded Software and Syst (HPCC,CSS,ICESS). IEEE. ISBN 978-1-4799-6123-8

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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.

Item Type: Book Section
Identification Number: https://doi.org/10.1109/HPCC.2014.143
Uncontrolled Keywords: learning process knowledge-base, semantic rule-based approach, process mining, personalised adaptive learning, automated learning systems, training purposes, educational purposes, organisations, schools, universities, further education centres, learning goals, automated learning, user interaction patterns, adaptive rules, captured user profiles, semantic activity logs annotation, semantic reasoning, reasoning methods
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 13:58
Last Modified: 22 Mar 2023 12:02
URI: https://www.open-access.bcu.ac.uk/id/eprint/4764

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