Dynamic Service Adaptation Framework for Context Aware Mobile Cloud Learning Using Semantic-Based Approach

Abstract

Current developments in IT involving mobile technology and cloud computing have also offered a modern way of teaching and learning. Effective learning methodologies that meet the learners' requirements as well as support learning resources (LR) with high flexibility, mobility and accessibility are currently the most key contribution of this technological advancement. Hence, Mobile Cloud Learning (MCL) has been introduced to boost the feature of m-learning that has already been on the market. MCL comprises of services that integrate with each other which are learning, assessment, feedback and analysis. These services need to be monitored, and continuous enhancement and evolvement are also required in the account of new learners’ requirements for the upgrading of services or maintaining the Quality of Services (QoS). Service-oriented systems need to be enhanced through a dynamic adaptation process during runtime to sense and react to the learners’ context or device’s context known as context awareness to provide a better user experience. This is where ontologies and rule-based approaches are used for context representation, descriptions, and reasoning mechanisms to support the semantic discovery and matching of services. QoS consideration is essential to ensure the efficiency and correctness of the services offered during the adaptation process. Thus, this paper describes dynamic adaptation framework that leverages on a semantic-based approach for context-aware mobile cloud learning. This framework aims to support in-service adaptation by considering contextual information and QoS using ontologies and rule-based approaches.Â