A Geo-Location Context-Aware Mobile Learning System with Adaptive Correlation Computing Methods

AbstractThis paper proposes a context-aware mobile learning system with adaptive correlation computing methods. This system enables users to enhance their knowledge by correlating it with daily experiences. The proposed system contains a hybrid metric vector space to define the correlation between heterogeneous metadata vectors of the user context and learning material. The system integrates heterogeneous metric vector spaces with definitions of the semantic relations between the vector spaces. The significant feature of this system is a hybrid adaptation mechanism for the calculation of correlation. The adaptation mechanism has multidirectional adaptation functions for various learning materials, situations, and learners. We propose a revise-localize-personalize (RLP) adaptation model. In the adaptation mechanism, users only have to improve the metadata or the relations just in their relevant field. The advantage of the system is that the system reduces the time-intensive efforts required for describing direct relations between user contexts and learning materials. This paper presents the feasibility of the context-aware heterogeneous information provision with the hybrid metric vector space, by implementing an actual mobile application system and examining real-world experiments on data provision

Investigation of a hierarchical context-aware architecture for rule-based customisation of mobile computing service

The continuous technical progress in mobile device built-in modules and embedded sensing techniques creates opportunities for context-aware mobile applications. The context-aware computing paradigm exploits the relevant context as implicit input to characterise the user and physical environment and provide a computing service customised to the contextual situation. However, heterogeneity in techniques, complexity of contextual situation, and gap between raw sensor data and usable context keep the techniques from truly integration for extensive use. Studies in this area mainly focus on feasibility demonstration of the emerging techniques, and they lack general architecture support and appropriate service customisation strategy. This investigation aims to provide general system architecture and technical approaches to deal with the heterogeneity problem and efficiently utilise the dynamic context towards proactive computing service that is customised to the contextual situation. The main efforts of this investigation are the approaches to gathering, handling, and utilising the dynamic context information in an efficient way and the decision making and optimisation methods for computing service customisation. In brief, the highlights of this thesis cover the following aspects: (1) a hierarchical context-aware computing architecture supporting interoperable distribution and further use of context; (2) an in-depth analysis and classification of context and the corresponding context acquisition methods; (3) context modelling and context data representation for efficient and interoperable use of context; (4) a rule-based service customisation strategy with a rule generation mechanism to supervise the service customisation. In addition, feasibility demonstration of the proposed system and contribution justification of this investigation are conducted through case studies and prototype implementations. One case study uses mobile built-in sensing techniques to improve the usability and efficiency of mobile applications constrained by resource limitation, and the other employs the mobile terminal and embedded sensing techniques to predict users’ expectations for home facility automatic control. Results demonstrate the feasibility of the proposed context handling architecture and service customisation methods. It shows great potential for employing the context of the computing environment for context-aware adaptation in pervasive and mobile applications but also indicates some underlying problems for further study