A context-adaptive model for mobile learning applications

Abstract

Emerging technologies, such as multimedia and mobile computing, require a high adaptable middleware platform which provides more flexible services to those applications in heterogeneous computing environments. To support persistent services in distributed wired/wireless environments for advanced learning systems, it is required for applications and middleware to be aware of the frequent and unpredictable changes in users' requirements as well as environmental conditions, and to be able to adapt their behavioural changes. One of the main limitations of current approaches for supporting adaptability is that applications themselves are responsible for triggering and adaptive mechanism when the underling infrastructure notifies them about any changes. In this paper, we present the designing a component-based context-adaptive model for context-aware mobile learning applications to support dynamic adaptation, and demonstrate our being developed mobile agents: adaptation, configuration, and meta agents, that help applications to adapt their computing environments according to rapidly changing contexts such as user-specific preference, application-specific preference, and low-level configurations.