Framework for Context-Aware Mobile Learning

点击翻译按钮获取中文摘要

Context-aware mobile learning is an important requirement for next generation intelligent e-learning systems. However, context-management in real world applications is a complex information processing task. In this research program, context reflects timeliness and mobility to nurture pervasive learning throughout an enterprise ecosystem. The aim is to dynamically provide useful learning resources on demand, anywhere, in a learner-driven context and on a learner's schedule. This is particularly important for corporate learning providers in order to make it possible for organizations to transform learning into a strategic lever. As learning material scale up and longer instruction times become common, knowledge acquisition become time-dependent and is increasingly acquired on the fly due to considerable changes in today's know-how economy requirements. However, the process of inferring, communicating, and presenting context-aware learning resources to mobile learners poses new challenges to the research community. This is mainly due to the limitations of mobile technology (i.e. limited bandwidth on wireless networks, and limited resources on mobile devices), and to the lack of cognitive mobile-learning models (i.e. problems related to domain-knowledge representation and contextual information retrieval, time constrained instruction, and human-computer interaction). This research program contributes towards solving some of the challenging problems in cognitive mobile-learning by making use of the progress in Semantic Web theory, e-learning standardization, and mobile computing. The approach is to develop a knowledge-centered framework based on ontological formulation that structures knowledge into meaningful content and creates an environment where software agents roaming around peer-to-peer learning object repositories can readily carry out sophisticated learning services discovery and compositions. The use of ontology aims at providing customized learning patterns which are delivered on-demand to the learner and/or combined with other patterns to form a self-adjusted learning route according to the learner's needs and available learning time.