Semantic web service adaptation model for a pervasive learning scenario

Abstract

With the proliferation of mobile devices, pervasive learning has become a new wave in technology-enhanced learning (TEL). One of the key problems to solve in this area is to adapt learning content and services according to a learner's needs and wants to different learning contexts at the workplace. We propose using semantic web services as a solution to context -adaptive pervasive learning. Another problem lies in web service search, which may return many services that do not match the requirements and context of a learner. This wastes resources and poses more problems to the formulation of just-in-time learning activities. In this paper, we define a service requirement specification to model a service request and a semantic description metadata schema to sufficiently annotate web service functionalities and behavior. On top of that, we propose an adaptation model to match and adapt relevant web services. The technique and algorithm presented in this paper are aimed at improving the efficiency and accuracy in selecting the right web service for a scenario at the workplace.